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Is the given time series strictly stationary? | [
"No",
"Yes"
] | No | binary | null | null | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 401 | [
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] |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Spike: the pattern of time series is distorted by random large spikes",
"Flip: the pattern is flipped at certain point in time",
"Speed up/down: the period of cyclic components is different from other parts of the time series"
] | Spike: the pattern of time series is distorted by random large spikes | multiple_choice | null | null | 64 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Spike Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 402 | [
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] |
Two time series are given, one with an upward trend and the other with a downward trend. Do they exhibit similar patterns aside from the trend? | [
"No, they have different cyclic components",
"Yes, they share a similar pattern"
] | No, they have different cyclic components | binary | [
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] | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Square Wave"
] | You should focus on the cyclic components of the time series. Do they have similar patterns aside from the trend? | Similarity Analysis | Shape | 403 | null |
You are given two time series where one is the lagged version of the other. What is the most likely lagging step? | [
"Lagging step is between 30 to 45",
"Lagging step is between 60 to 75",
"Lagging step is between 5 to 10"
] | Lagging step is between 30 to 45 | multiple_choice | [
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] | 98 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series. | Causality Analysis | Granger Causality | 404 | null |
What is the primary cyclic pattern observed in the time series? | [
"SawtoothWave",
"No Pattern at all",
"SineWave",
"SquareWave"
] | No Pattern at all | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 405 | [
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] |
Is the two time series lagged version of each other despite minor noise? | [
"Yes, they are lagged versions",
"No, they are not lagged versions at all"
] | No, they are not lagged versions at all | binary | [
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] | 100 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair",
"Red Noise"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the noise. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 406 | null |
One type of noise in time series is random walk. Is the given time series noisy (noise dominates other patterns) based on your understanding of random walk | [
"No",
"Yes"
] | Yes | binary | null | null | 56 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. When the time series has a random walk noise applied to it, it seems like the pattern are even more disrupted. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 407 | [
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] |
Is the given time series likely to be a random walk process? | [
"Yes",
"No"
] | Yes | binary | null | null | 53 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | Random walk is a non-stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. Another important property is that the noise is correlated over time. Does the time series seem to have these properties? | Noise Understanding | Red Noise Recognition | 408 | [
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] |
Is time series 2 a lagged version of time series 1? | [
"Yes",
"No, they do not share similar pattern",
"No, time series 1 is a lagged version of time series 2"
] | No, they do not share similar pattern | multiple_choice | [
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] | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 409 | null |
The given time series has a trend and a cyclic component. It also has an anomaly. What is the most likely combination of components without the anomaly? | [
"Log trend and sawtooth wave",
"Linear trend and sine wave",
"Exponential trend and square wave"
] | Log trend and sawtooth wave | multiple_choice | null | null | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Exponential Trend",
"Square Wave",
"Log Trend",
"Sawtooth Wave",
"Cutoff Anomaly",
"Flip Anomaly"
] | The anomaly only influences a small part of the time series. You should focus on the overall pattern of the time series without the anomaly. Can you recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 410 | [
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] |
What is the most likely mean of the given time series? | [
"24.24",
"5.17",
"-19.11"
] | -19.11 | multiple_choice | null | null | 41 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Mean"
] | The given time series is stationary. Check the average value of the time series over time. | Pattern Recognition | First Two Moment Recognition | 411 | [
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Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude remain the same over time",
"Amplitude decrease over time",
"Amplitude increase over time"
] | Amplitude remain the same over time | multiple-choice | null | null | 28 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 412 | [
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] |
Which additive combination of patterns best describes the time series? | [
"SawtoothWave + SquareWave",
"SineWave + SquareWave",
"SineWave + SawtoothWave"
] | SineWave + SawtoothWave | multiple-choice | null | null | 16 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition"
] | Imagine the shape of the time series as addition of two different patterns. | Pattern Recognition | Cycle Recognition | 413 | [
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The given time series is a gaussian white noise process. What is the most likely noise level (variance)? | [
"1.57",
"8.18",
"3.46"
] | 1.57 | multiple_choice | null | null | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 414 | [
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] |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with speed up/down anomaly: the period of cyclic components is different from other parts of the time series",
"Time series 1 with flip anomaly: the pattern is flipped at certain point in time",
"Time series 2 with cutoff anomaly: the pattern of time series disappeared for certain point in time and became flat"
] | Time series 1 with flip anomaly: the pattern is flipped at certain point in time | multiple_choice | [
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] | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 415 | null |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"7.26",
"2.75",
"17.62"
] | 7.26 | multiple-choice | null | null | 23 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 416 | [
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] |
The time series has a trend and cyclic component added together. Which components are most likely present in the given time series? | [
"Linear trend and sine wave",
"No trend and sawtooth wave",
"Exponential trend and sine wave"
] | Linear trend and sine wave | multiple-choice | null | null | 26 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Sine Wave",
"Sawtooth Wave",
"Additive Composition"
] | For trend, check if the slope is constant or changes over time. For cyclic component, check the overall shape of the time series. | Pattern Recognition | Cycle Recognition | 417 | [
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] |
Does the given time series exhibit any monotonic increasing trend? | [
"Yes",
"No"
] | No | binary | null | null | 3 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Check if the time series values increase over time. | Pattern Recognition | Trend Recognition | 418 | [
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Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude increase over time",
"Amplitude decrease over time",
"Amplitude remain the same over time"
] | Amplitude decrease over time | multiple-choice | null | null | 28 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 419 | [
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] |
The given time series is a gaussian white noise process. What is the most likely noise level (variance)? | [
"6.29",
"1.53",
"4.78"
] | 1.53 | multiple_choice | null | null | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 420 | [
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] |
The given time series has a cycle component and a trend component. Is it an additive or multiplicative model between the two components? | [
"Multiplicative",
"Additive"
] | Multiplicative | multiple_choice | null | null | 11 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Additive Composition",
"Multiplicative Composition"
] | For a multiplicative composition, the amplitude of the cyclic component will increase or decrease depending on the trend component. | Pattern Recognition | Trend Recognition | 421 | [
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] |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"No, time series 1 has exponential trend and time series 2 has log trend",
"No, time series 1 has linear trend and time series 2 has exponential trend",
"Yes, they both have exponential trend"
] | No, time series 1 has exponential trend and time series 2 has log trend | multiple_choice | [
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] | 85 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 422 | null |
Is time series 1 a lagged version of time series 2? | [
"No, time series 2 is a lagged version of time series 1",
"No, they do not share similar pattern",
"Yes"
] | Yes | multiple_choice | [
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] | 97 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 423 | null |
Two time series are given, one with an upward trend and the other with a downward trend. Do they exhibit similar patterns aside from the trend? | [
"Yes, they share a similar pattern",
"No, they have different cyclic components"
] | Yes, they share a similar pattern | binary | [
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] | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Square Wave"
] | You should focus on the cyclic components of the time series. Do they have similar patterns aside from the trend? | Similarity Analysis | Shape | 424 | null |
Which of the following time series is more likely to be an AR(1) process? | [
"Time Series 1",
"Time Series 2"
] | Time Series 1 | multiple_choice | [
2.6897846036867223,
5.2552917481898085,
19.837316873693847,
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18.826735652986,
1.9555235957328811,
16.95678947451932,
14.326716516813955,
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19.765907676521383,
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] | 48 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Stationarity"
] | AR(1) process is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. The other option is likely not stationary. | Pattern Recognition | AR/MA recognition | 425 | null |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Spike: the pattern of time series is distorted by random large spikes",
"Flip: the pattern is flipped at certain point in time",
"Speed up/down: the period of cyclic components is different from other parts of the time series"
] | Spike: the pattern of time series is distorted by random large spikes | multiple_choice | null | null | 64 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Spike Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 426 | [
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You are seeing two instances of random walk. Are they likely to have the same variance? | [
"No, time series 2 has higher variance",
"No, time series 1 has higher variance",
"Yes, they have the same variance"
] | No, time series 1 has higher variance | multiple_choice | [
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] | 93 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Variance"
] | Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series. | Similarity Analysis | Distributional | 427 | null |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly | multiple_choice | [
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] | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 428 | null |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Sine wave with linear trend",
"Sawtooth wave with exponential trend",
"Square wave with log trend"
] | Square wave with log trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 429 | [
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] |
Based on the given time series, how many different regimes are there? | [
"3",
"1",
"4"
] | 3 | multiple_choice | null | null | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 430 | [
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] |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Exponential -> Linear -> Log",
"Linear -> Exponential -> Log",
"Log",
"Linear -> Exponential"
] | Linear -> Exponential | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 431 | [
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] |
Is time series 1 a lagged version of time series 2? | [
"No, they do not share similar pattern",
"No, time series 2 is a lagged version of time series 1",
"Yes"
] | No, they do not share similar pattern | multiple_choice | [
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] | 97 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 432 | null |
Does the given two time series have similar pattern? | [
"No, they have different seasonable pattern",
"Yes, they have similar seasonal pattern"
] | Yes, they have similar seasonal pattern | binary | [
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] | 78 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 433 | null |
Both time series have a cyclic components. Which time series has a higher amplitude of the cyclic component? | [
"Time series 2 has higher amplitude",
"Time series 1 has higher amplitude"
] | Time series 1 has higher amplitude | binary | [
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] | 83 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 434 | null |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"Negative autocorrelation around -0.8",
"High positive autocorrelation around 0.8",
"No autocorrelation"
] | High positive autocorrelation around 0.8 | multiple_choice | null | null | 45 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 435 | [
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] |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal",
"No, time series 2 granger causes time series 1"
] | No, time series 2 granger causes time series 1 | binary | [
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] | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 436 | null |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Cutoff: the pattern of time series disappeared for certain point in time and became flat",
"Scale: the pattern is at obviously different scale at certain point in time"
] | Cutoff: the pattern of time series disappeared for certain point in time and became flat | multiple_choice | null | null | 65 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Scale Anomaly",
"Wander Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 437 | [
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] |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Cutoff: the pattern of time series disappeared for certain point in time and became flat",
"Scale: the pattern is at obviously different scale at certain point in time"
] | Scale: the pattern is at obviously different scale at certain point in time | multiple_choice | null | null | 65 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Scale Anomaly",
"Wander Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 438 | [
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] |
There are two time series given. Is one of them a scaled version of the other? | [
"Yes, time series 1 is a scaled version of time series 2",
"Yes, time series 2 is a scaled version of time series 1",
"No, they do not share similar pattern"
] | Yes, time series 1 is a scaled version of time series 2 | binary | [
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] | 86 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Moving Average Process"
] | Scaled version refers to the same pattern but with different amplitude. You should check if the pattern is the same for both time series. If they are the same, you should check the amplitude of the cyclic component. | Similarity Analysis | Shape | 439 | null |
Covariance stationarity in a time series means constant mean, constant variance and that autocovariance depends only on time lag, not absolute time. Is the given time series covariance-stationary? | [
"Yes",
"No"
] | Yes | binary | null | null | 36 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"AutoRegressive Process",
"Linear Trend"
] | Check if the covariance between any two points depends only on the time distance between them. | Pattern Recognition | Stationarity Detection | 440 | [
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The given time series is a random walk process. What is the most likely noise level (variance) at each step? | [
"1.89",
"18.29"
] | 18.29 | multiple_choice | null | null | 54 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the past value. | Noise Understanding | Red Noise Recognition | 441 | [
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] |
Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity? | [
"No, the mean is different overtime",
"No, the variance is different overtime",
"Yes"
] | No, the mean is different overtime | multiple_choice | null | null | 33 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | For mean, check if the average value changes over time. For variance, check if the degree of variation changes over time. | Pattern Recognition | Stationarity Detection | 442 | [
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] |
The given time series has an increasing trend, is it a linear trend or log trend? | [
"Log",
"Linear"
] | Log | multiple_choice | null | null | 7 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Log Trend"
] | Check if the slope of the time series is constant or changes over time. | Pattern Recognition | Trend Recognition | 443 | [
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One type of noise in time series is random walk. Is the given time series noisy (noise dominates other patterns) based on your understanding of random walk | [
"No",
"Yes"
] | Yes | binary | null | null | 56 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. When the time series has a random walk noise applied to it, it seems like the pattern are even more disrupted. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 444 | [
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] |
You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component? | [
"Time series 1",
"Time series 2"
] | Time series 1 | multiple_choice | [
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] | 61 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Variance"
] | The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average? | Noise Understanding | Signal to Noise Ratio Understanding | 445 | null |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal",
"No, time series 2 granger causes time series 1"
] | No, they are not granger causal | binary | [
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] | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 446 | null |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"2",
"1",
"4"
] | 4 | multiple_choice | null | null | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 447 | [
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] |
There are two time series given. Is one of them a scaled version of the other? | [
"Yes, time series 1 is a scaled version of time series 2",
"Yes, time series 2 is a scaled version of time series 1",
"No, they do not share similar pattern"
] | No, they do not share similar pattern | binary | [
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] | 86 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Moving Average Process"
] | Scaled version refers to the same pattern but with different amplitude. You should check if the pattern is the same for both time series. If they are the same, you should check the amplitude of the cyclic component. | Similarity Analysis | Shape | 448 | null |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Linear -> Exponential",
"Exponential -> Linear -> Log",
"Linear -> Exponential -> Log",
"Log"
] | Log | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 449 | [
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] |
What is the primary cyclic pattern observed in the time series? | [
"SineWave",
"No Pattern at all",
"SquareWave",
"SawtoothWave"
] | SawtoothWave | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 450 | [
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] |
Is the two time series lagged version of each other despite amplitude difference? | [
"Yes, they are lagged versions",
"No, they are not lagged versions"
] | No, they are not lagged versions | binary | [
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] | 102 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 451 | null |
Is the given time series likely to be stationary after differencing? | [
"No",
"Yes"
] | No | binary | null | null | 31 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Differencing is a common technique to make a time series stationary. Focus on checking if the trend is removed after differencing. | Pattern Recognition | Stationarity Detection | 452 | [
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] |
The given time series is a random walk process. What is the most likely noise level (variance) at each step? | [
"1.93",
"15.47"
] | 15.47 | multiple_choice | null | null | 54 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the past value. | Noise Understanding | Red Noise Recognition | 453 | [
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] |
The given time series is a square wave. What is the most likely period of the square wave? | [
"16.69",
"57.32",
"95.26"
] | 16.69 | multiple-choice | null | null | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Square Wave",
"Period"
] | Check the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 454 | [
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] |
What is the primary cyclic pattern observed in the time series? | [
"SquareWave",
"No Pattern at all",
"SineWave",
"SawtoothWave"
] | SineWave | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 455 | [
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] |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly | multiple_choice | [
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] | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 456 | null |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Sawtooth wave with exponential trend",
"Square wave with log trend",
"Sine wave with linear trend"
] | Square wave with log trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 457 | [
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] |
Despite the noise, does the given two time series have similar pattern? | [
"No, they have different seasonal pattern",
"Yes, they have similar seasonal pattern"
] | No, they have different seasonal pattern | binary | [
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] | 79 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 458 | null |
What is the most likely linear trend coefficient of the given time series? Linear trend coefficient here refers to the end value of the linear trend. | [
"1.3",
"0",
"6.44"
] | 0 | multiple_choice | null | null | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 459 | [
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] |
Is the two time series lagged version of each other despite amplitude difference? | [
"Yes, they are lagged versions",
"No, they are not lagged versions"
] | Yes, they are lagged versions | binary | [
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] | 99 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 460 | null |
Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity? | [
"Yes",
"No, the mean is different overtime",
"No, the variance is different overtime"
] | No, the mean is different overtime | multiple_choice | null | null | 33 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | For mean, check if the average value changes over time. For variance, check if the degree of variation changes over time. | Pattern Recognition | Stationarity Detection | 461 | [
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Are the two time series flipped versions of each other despite noise? | [
"Yes, they are flipped versions",
"No, they are not flipped versions"
] | No, they are not flipped versions | binary | [
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] | 90 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Both time series have a trend and a cyclic component. Then we say two time series are flipped versions of each other, we mean that the sign of each step is flipped. You should check if the sign of each step is flipped for both time series. At a high level, you should check if the time series are mirror images of each other. | Similarity Analysis | Shape | 462 | null |
Are the given two time series likely to have the same underlying distribution? | [
"No, they have different underlying distribution",
"Yes, they have the same underlying distribution: Gaussian White Noise"
] | No, they have different underlying distribution | binary | [
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] | 91 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise"
] | You should focus on the underlying distribution of the time series. You can start from analyzing whether both time series are stationary. Then, you can check if they have the same mean and degree of variation from mean. | Similarity Analysis | Distributional | 463 | null |
Despite the noise, does the given two time series have similar pattern? | [
"Yes, they have similar seasonal pattern",
"No, they have different seasonal pattern"
] | Yes, they have similar seasonal pattern | binary | [
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] | 79 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 464 | null |
How does the linear trend in the first half of the time series compare to the trend in the second half? | [
"Same",
"Different"
] | Different | binary | null | null | 6 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series is a piecewise linear trend with different slopes in the first and second half. | Pattern Recognition | Trend Recognition | 465 | [
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] |
Is the given time series likely to be stationary after removing the trend? | [
"Yes",
"No"
] | No | binary | null | null | 34 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Exponential Trend"
] | Trend brings the overall shape of the time series up or down. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 466 | [
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] |
You are given two AR(1) process, which one of them is more likely to have a larger magnitude in autocorrelation at lag 1? | [
"Time Series 2",
"Time Series 1"
] | Time Series 1 | multiple_choice | [
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] | 47 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation",
"AutoRegressive Process"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. You should compare the two time series to see which one has a larger magnitude in autocorrelation at lag 1. | Pattern Recognition | AR/MA recognition | 467 | null |
Is time series 2 a lagged version of time series 1? | [
"Yes",
"No, time series 1 is a lagged version of time series 2",
"No, they do not share similar pattern"
] | Yes | multiple_choice | [
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] | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 468 | null |
You are seeing two instances of random walk. Are they likely to have the same variance? | [
"No, time series 1 has higher variance",
"Yes, they have the same variance",
"No, time series 2 has higher variance"
] | Yes, they have the same variance | multiple_choice | [
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] | 93 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Variance"
] | Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series. | Similarity Analysis | Distributional | 469 | null |
You are given two time series which both have a trend component. Do they share the same direction of trend (upward or downward)? | [
"No, they have different direction of trend",
"Yes, they have the same direction of trend"
] | No, they have different direction of trend | binary | [
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] | 81 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave"
] | Trend refers to the general direction of the time series. Are the values going up or down? Check this for both time series to see if they have the same direction of trend. | Similarity Analysis | Shape | 470 | null |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"Negative autocorrelation around -0.8",
"High positive autocorrelation around 0.8",
"No autocorrelation"
] | Negative autocorrelation around -0.8 | multiple_choice | null | null | 45 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 471 | [
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The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Exponential -> Linear -> Log",
"Linear -> Exponential -> Log",
"Linear -> Exponential",
"Log"
] | Exponential -> Linear -> Log | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 472 | [
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] |
How does the noise in the given time series influence the detection of periodic pattern in the time series? | [
"No influence, Sinewave",
"Distort the pattern"
] | Distort the pattern | binary | null | null | 58 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Sine Wave",
"Additive Composition"
] | When the noise level is high, it can distort the pattern in the time series. Can you check if you can still detect the cyclic pattern in the time series? | Noise Understanding | Signal to Noise Ratio Understanding | 473 | [
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] |
Is the two time series lagged version of each other despite amplitude difference? | [
"No, they are not lagged versions",
"Yes, they are lagged versions"
] | Yes, they are lagged versions | binary | [
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] | 99 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 474 | null |
Are the given two time series likely to have the same underlying distribution? | [
"No, they have different underlying distribution",
"Yes, they have the same underlying distribution"
] | No, they have different underlying distribution | binary | [
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] | 95 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"AutoRegressive Process",
"Linear Trend"
] | When we say two time series have the same underlying distribution, you should check if they have the same mean and variance. They should also share similar behaviors over time. | Similarity Analysis | Distributional | 475 | null |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with speed up/down anomaly: the period of cyclic components is different from other parts of the time series",
"Time series 2 with cutoff anomaly: the pattern of time series disappeared for certain point in time and became flat",
"Time series 1 with flip anomaly: the pattern is flipped at certain point in time"
] | Time series 1 with flip anomaly: the pattern is flipped at certain point in time | multiple_choice | [
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] | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 476 | null |
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series? | [
"Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff anomaly and time series 2 with flip anomaly"
] | Time series 1 with cutoff anomaly and time series 2 with flip anomaly | multiple_choice | [
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] | 74 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 477 | null |
Seasonal stationarity refers to a time series where statistical properties remain constant within seasons but may vary between seasons. Does the time series exhibit seasonal stationarity? | [
"No",
"Yes"
] | No | binary | null | null | 37 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Sine Wave",
"Linear Trend",
"Gaussian White Noise"
] | Determine if the statistical properties of the series are constant within seasons across years. | Pattern Recognition | Stationarity Detection | 478 | [
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] |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 2 with cutoff anomaly: the pattern of time series disappeared for certain point in time and became flat",
"Time series 1 with flip anomaly: the pattern is flipped at certain point in time",
"Time series 1 with speed up/down anomaly: the period of cyclic components is different from other parts of the time series"
] | Time series 1 with flip anomaly: the pattern is flipped at certain point in time | multiple_choice | [
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] | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 479 | null |
Two time series are given. Both of them have a noise component. Do they have the same type of noise? | [
"No, they have different noise: white noise and red noise",
"Yes, they both have Gaussian white noise"
] | No, they have different noise: white noise and red noise | binary | [
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] | 87 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise",
"Additive Composition"
] | When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns. | Similarity Analysis | Shape | 480 | null |
Two time series are given. Both of them have a noise component. Do they have the same level (variance) of noise? | [
"Yes, they both have the same level of noise",
"No, they have different level of noise"
] | Yes, they both have the same level of noise | binary | [
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] | 88 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Variance"
] | Noise level refers to the amplitude of the random fluctuations in the time series. Both time series have a white noise component added to it. You should check the amplitude of the noise for both time series. | Similarity Analysis | Shape | 481 | null |
Based on the given time series, how many different regimes are there? | [
"1",
"4",
"3"
] | 3 | multiple_choice | null | null | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 482 | [
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The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Linear -> Exponential -> Log",
"Linear -> Exponential",
"Exponential -> Linear -> Log",
"Log"
] | Linear -> Exponential | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 483 | [
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] |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"2.03",
"6.02",
"17.43"
] | 17.43 | multiple-choice | null | null | 24 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | After the sine wave, the square wave follows. Begin by identifying where the square wave starts. Next, measure the distance between its peak and baseline. | Pattern Recognition | Cycle Recognition | 484 | [
0.15093141062218893,
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] |
Is the given time series likely to have a non-stationary anomaly? | [
"Yes, due to trend reversal",
"No, the anomaly is stationary (white noise)",
"Yes, due to cutoff"
] | Yes, due to cutoff | binary | null | null | 69 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Sine Wave",
"Cutoff Anomaly",
"Spike Anomaly"
] | Non-stationary anomaly refers to the anomaly that changes over time. You should check if the time series has a constant mean and variance over time. If not, you should check the type of anomaly based on the given definitions. For example, spikes anomaly are stationary. | Anolmaly Detection | General Anomaly Detection | 485 | [
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] |
You are given two time series which both have a trend component. Do they share the same direction of trend (upward or downward)? | [
"No, they have different direction of trend",
"Yes, they have the same direction of trend"
] | No, they have different direction of trend | binary | [
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] | 81 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave"
] | Trend refers to the general direction of the time series. Are the values going up or down? Check this for both time series to see if they have the same direction of trend. | Similarity Analysis | Shape | 486 | null |
Are the two time series flipped versions of each other despite noise? | [
"Yes, they are flipped versions",
"No, they are not flipped versions"
] | Yes, they are flipped versions | binary | [
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] | 90 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Both time series have a trend and a cyclic component. Then we say two time series are flipped versions of each other, we mean that the sign of each step is flipped. You should check if the sign of each step is flipped for both time series. At a high level, you should check if the time series are mirror images of each other. | Similarity Analysis | Shape | 487 | null |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal",
"No, time series 2 granger causes time series 1"
] | Yes, time series 1 granger causes time series 2 | binary | [
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] | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 488 | null |
Two time series are given, one with an upward trend and the other with a downward trend. Do they exhibit similar patterns aside from the trend? | [
"Yes, they share a similar pattern",
"No, they have different cyclic components"
] | Yes, they share a similar pattern | binary | [
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] | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Square Wave"
] | You should focus on the cyclic components of the time series. Do they have similar patterns aside from the trend? | Similarity Analysis | Shape | 489 | null |
The following time series has an anomaly. What is the most likely type of anomaly? | [
"Cutoff: the pattern of time series disappeared for certain point in time and became flat",
"Scale: the pattern is at obviously different scale at certain point in time"
] | Scale: the pattern is at obviously different scale at certain point in time | multiple_choice | null | null | 65 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Scale Anomaly",
"Wander Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 490 | [
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] |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Exponential",
"Linear",
"Log"
] | Linear | multiple_choice | null | null | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 491 | [
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] |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"No autocorrelation",
"High positive autocorrelation around 0.8",
"Negative autocorrelation around -0.8"
] | High positive autocorrelation around 0.8 | multiple_choice | null | null | 45 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 492 | [
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] |
Does the given time series exhibit regime switching? | [
"No",
"Yes"
] | No | binary | null | null | 39 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | Identify whether the time series exhibit different patterns over time. | Pattern Recognition | Regime Switching Detection | 493 | [
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] |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"1",
"4",
"2"
] | 4 | multiple_choice | null | null | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 494 | [
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] |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"6.99",
"17.71",
"1.39"
] | 6.99 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 495 | [
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The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Log",
"Exponential",
"Linear"
] | Exponential | multiple_choice | null | null | 10 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend",
"Sine Wave",
"Additive Composition"
] | Despite having a cyclic component, check the general trend of the time series. | Pattern Recognition | Trend Recognition | 496 | [
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] |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"No, time series 1 has exponential trend and time series 2 has log trend",
"No, time series 1 has linear trend and time series 2 has exponential trend",
"Yes, they both have exponential trend"
] | Yes, they both have exponential trend | multiple_choice | [
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] | 85 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 497 | null |
Is the two time series lagged version of each other despite amplitude difference? | [
"No, they are not lagged versions",
"Yes, they are lagged versions"
] | Yes, they are lagged versions | binary | [
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] | 99 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 498 | null |
Is time series 2 a lagged version of time series 1? | [
"Yes",
"No, time series 1 is a lagged version of time series 2",
"No, they do not share similar pattern"
] | Yes | multiple_choice | [
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] | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 499 | null |
Does time series 1 granger cause time series 2? | [
"No, they are not granger causal",
"Yes, time series 1 granger causes time series 2",
"No, time series 2 granger causes time series 1"
] | Yes, time series 1 granger causes time series 2 | binary | [
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] | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 500 | null |
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