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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 | 1 | null |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Increase",
"Remain the same",
"Decrease"
] | Decrease | multiple-choice | null | null | 17 | 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"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 2 | [
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] |
Does the given two time series have similar pattern? | [
"Yes, they have similar seasonal pattern",
"No, they have different seasonable 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 | 3 | null |
You are given two time series which both have upward trend. Which time series has a higher slope in terms of magnitude? | [
"Time series 1 has higher slope",
"Time series 2 has higher slope"
] | Time series 1 has higher slope | binary | [
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] | 80 | 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",
"Sine Wave",
"Sawtooth Wave"
] | Slope refers to the steepness of the trend. You should check the direction of the trend and the steepness of the trend. If the trend is upward, you should check the magnitude of the slope. | Similarity Analysis | Shape | 4 | null |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution: Gaussian White Noise",
"No, they have different underlying distribution"
] | Yes, they have the same underlying distribution: Gaussian White Noise | 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 | 5 | null |
The given time series has an increasing trend, is it a linear trend or log trend? | [
"Linear",
"Log"
] | Linear | 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 | 6 | [
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] |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution: AR(1)",
"No, they have different underlying distribution: AR(1) and MA(5)"
] | No, they have different underlying distribution: AR(1) and MA(5) | binary | [
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] | 92 | 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",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 7 | 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 | 8 | 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 | 9 | null |
What is the type of the trend of the given time series? | [
"Exponential",
"Linear",
"No Trend"
] | Exponential | multiple_choice | null | null | 1 | 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"
] | It would be helpful to check if slope of the time series changes over time. | Pattern Recognition | Trend Recognition | 10 | [
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] |
What type of trend does the time series exhibit in the latter half? | [
"Exponential",
"Linear",
"No trend"
] | No trend | multiple_choice | null | null | 14 | medium | 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"
] | Focus on the pattern of growth or decline in the second half of the time series. | Pattern Recognition | Trend Recognition | 11 | [
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Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Additive",
"Multiplicative"
] | Multiplicative | binary | null | null | 59 | 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. | [
"Additive Composition",
"Multiplicative Composition",
"Gaussian White Noise"
] | Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a cyclic component is added with a white noise, the cyclic pattern still remains. When a cyclic component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 12 | [
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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 | 13 | null |
The given time series has square wave pattern. How does its period change from the beginning to the end? | [
"Decrease",
"Remain the same",
"Increase"
] | Decrease | multiple-choice | null | null | 18 | 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. | [
"Square Wave",
"Period"
] | Base on the definition of period, check if the time interval between two peaks remains the same. | Pattern Recognition | Cycle Recognition | 14 | [
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] |
Which additive combination of patterns best describes the time series? | [
"SineWave + SquareWave",
"SawtoothWave + SquareWave",
"SineWave + SawtoothWave"
] | SawtoothWave + SquareWave | 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 | 15 | [
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] |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"13.92",
"58.31",
"37.98"
] | 58.31 | multiple-choice | null | null | 25 | 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. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 16 | [
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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 increase over time",
"Amplitude decrease 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 | 17 | [
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] |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"No",
"Yes"
] | Yes | binary | null | null | 32 | 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"
] | You can try to identify different parts in the time series first, and see if any part is stationary. | Pattern Recognition | Stationarity Detection | 18 | [
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] |
Is the given time series likely to be stationary after differencing? | [
"No",
"Yes"
] | Yes | 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 | 19 | [
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] |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"Speedup: the period of cyclic components is different from other parts of the time series and Cutoff: the pattern of time series disappeared for certain point in time and became flat",
"Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time",
"Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time"
] | Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 68 | 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 should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 20 | [
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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 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 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 | multiple_choice | [
0,
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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 | 21 | null |
There are two time series given. Is one of them a scaled version of the other? | [
"Yes, time series 2 is a scaled version of time series 1",
"Yes, time series 1 is a scaled version of time series 2",
"No, they do not share similar pattern"
] | Yes, time series 2 is a scaled version of time series 1 | binary | [
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-12.49160992649376,
-11.974574886320672,
-10.859419522704027,
-9.069954001330416,
-7.228729907584416,
-5.030414731771099,
-2.583651801784304,
-0.09372126126859047,
2.666632135236459,
5.20192421598514,
7.396742376838502
] | 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 | 22 | null |
Which of the following time series is more likely to be an MA(1) process? | [
"Time Series 1",
"Time Series 2"
] | Time Series 2 | multiple_choice | [
0.01601035334480108,
0.030933701131625833,
0.017813055109762464,
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] | 49 | 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. | [
"Moving Average Process",
"Stationarity"
] | MA(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 | 23 | null |
Is the given time series likely to have a non-stationary anomaly? | [
"Yes, due to cutoff",
"Yes, due to trend reversal",
"No, the anomaly is stationary (white noise)"
] | Yes, due to trend reversal | 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 | 24 | [
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] |
What is the most likely variance of the given time series? | [
"0",
"0.77",
"varies across time"
] | varies across time | multiple_choice | null | null | 42 | 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. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 25 | [
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The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Decrease",
"Increase",
"Remain the same"
] | Increase | multiple-choice | null | null | 19 | 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"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 26 | [
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] |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution",
"No, they have different underlying distribution"
] | Yes, they have the same 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 | 27 | null |
What is the most likely variance of the given time series? | [
"varies across time",
"0.63",
"0"
] | varies across time | multiple_choice | null | null | 42 | 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. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 28 | [
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] |
What is the most likely variance of the given time series? | [
"0.88",
"varies across time",
"0"
] | 0 | multiple_choice | null | null | 42 | 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. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 29 | [
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] |
Is the given time series a white noise process? | [
"Yes",
"No"
] | Yes | binary | null | null | 50 | 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"
] | White noise 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. Another important property is that the noise is uncorrelated over time. Does the time series seem to have these properties? | Noise Understanding | White Noise Recognition | 30 | [
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] |
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"
] | No, they have different 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 | 31 | null |
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 | 32 | [
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The following time series has a noise component, a trend component, and a cyclic component. Is the noise component more likely to be a white noise or random walk? | [
"White Noise",
"Random Walk"
] | Random Walk | binary | null | null | 52 | 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",
"Gaussian White Noise"
] | White noise 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. This can help you distinguish between white noise and random walk. | Noise Understanding | White Noise Recognition | 33 | [
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] |
What is the type of the trend of the given time series? | [
"No Trend",
"Exponential",
"Linear"
] | Linear | multiple_choice | null | null | 1 | 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"
] | It would be helpful to check if slope of the time series changes over time. | Pattern Recognition | Trend Recognition | 34 | [
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] |
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",
"Exponential trend and square wave",
"Linear trend and sine wave"
] | Exponential trend and square 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 | 35 | [
1,
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] |
What type of trend does the time series exhibit in the latter half? | [
"Linear",
"No trend",
"Exponential"
] | Linear | multiple_choice | null | null | 14 | medium | 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"
] | Focus on the pattern of growth or decline in the second half of the time series. | Pattern Recognition | Trend Recognition | 36 | [
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] |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"5.77",
"2.09",
"15.98"
] | 2.09 | 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 | 37 | [
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] |
You are given two time series which both have upward trend. Which time series has a higher slope in terms of magnitude? | [
"Time series 2 has higher slope",
"Time series 1 has higher slope"
] | Time series 1 has higher slope | binary | [
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] | 80 | 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",
"Sine Wave",
"Sawtooth Wave"
] | Slope refers to the steepness of the trend. You should check the direction of the trend and the steepness of the trend. If the trend is upward, you should check the magnitude of the slope. | Similarity Analysis | Shape | 38 | null |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"Negative autocorrelation around -0.8",
"No autocorrelation",
"High positive autocorrelation around 0.8"
] | 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 | 39 | [
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] |
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 | [
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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 | 40 | null |
What is the most likely variance of the given time series? | [
"varies across time",
"0.99",
"0"
] | 0 | multiple_choice | null | null | 42 | 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. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 41 | [
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] |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal"
] | 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 | 42 | 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 | 43 | [
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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"
] | 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 | 44 | null |
Does the trend of the time series change sign or direction at any point? | [
"No",
"Yes"
] | Yes | binary | null | null | 12 | medium | 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"
] | Check if the overall direction of the time series changes at any point. | Pattern Recognition | Trend Recognition | 45 | [
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] |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"Yes, they both have exponential trend",
"No, time series 1 has linear trend and time series 2 has exponential trend",
"No, time series 1 has exponential trend and time series 2 has log 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 | 46 | null |
Does the trend of the time series change sign or direction at any point? | [
"Yes",
"No"
] | Yes | binary | null | null | 12 | medium | 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"
] | Check if the overall direction of the time series changes at any point. | Pattern Recognition | Trend Recognition | 47 | [
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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, time series 1 is a lagged version of time series 2 | 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 | 48 | null |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"53.3",
"10.54",
"30.59"
] | 10.54 | multiple-choice | null | null | 25 | 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. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 49 | [
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] |
The time series shows a structural break. What is the most likely cause of this break? | [
"Change in variance in underlying distribution",
"Abrupt frequency change",
"Sudden shift in trend direction"
] | Sudden shift in trend direction | multiple_choice | null | null | 71 | 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",
"Gaussian White Noise",
"Sine Wave"
] | You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options. | Anolmaly Detection | General Anomaly Detection | 50 | [
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Is the given time series likely to have a non-stationary anomaly? | [
"No, the anomaly is stationary (white noise)",
"Yes, due to trend reversal",
"Yes, due to cutoff"
] | No, the anomaly is stationary (white noise) | 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 | 51 | [
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Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Additive",
"Multiplicative"
] | Additive | binary | null | null | 57 | 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. | [
"Additive Composition",
"Multiplicative Composition",
"Gaussian White Noise"
] | Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a trend component is added with a white noise, the general trend still remains. When a trend component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 52 | [
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] |
Is the given time series likely to have a non-stationary anomaly? | [
"No, the anomaly is stationary (white noise)",
"Yes, due to cutoff",
"Yes, due to trend reversal"
] | Yes, due to trend reversal | 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 | 53 | [
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] |
What is the type of the trend of the given time series? | [
"Linear",
"Exponential",
"No Trend"
] | Exponential | multiple_choice | null | null | 1 | 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"
] | It would be helpful to check if slope of the time series changes over time. | Pattern Recognition | Trend Recognition | 54 | [
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] |
The given time series has square wave pattern. How does its period change from the beginning to the end? | [
"Increase",
"Decrease",
"Remain the same"
] | Increase | multiple-choice | null | null | 18 | 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. | [
"Square Wave",
"Period"
] | Base on the definition of period, check if the time interval between two peaks remains the same. | Pattern Recognition | Cycle Recognition | 55 | [
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] |
What type of noise is present in the given time series? | [
"No significant noise",
"Gaussian White Noise",
"Red Noise"
] | No significant noise | multiple_choice | null | null | 62 | medium | 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"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 56 | [
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] |
The time series shows a structural break. What is the most likely cause of this break? | [
"Abrupt frequency change",
"Sudden shift in trend direction",
"Change in variance in underlying distribution"
] | Change in variance in underlying distribution | multiple_choice | null | null | 71 | 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",
"Gaussian White Noise",
"Sine Wave"
] | You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options. | Anolmaly Detection | General Anomaly Detection | 57 | [
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] |
Is the given time series likely to be stationary after removing the trend? | [
"Yes",
"No"
] | Yes | 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 | 58 | [
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] |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"8.46",
"2.04",
"17.7"
] | 8.46 | 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 | 59 | [
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] |
How does the linear trend in the first half of the time series compare to the trend in the second half? | [
"Different",
"Same"
] | 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 | 60 | [
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] |
Is the mean stable over time in the given time series? | [
"No",
"Yes"
] | No | binary | null | null | 43 | 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"
] | Check if the average value of the time series changes over time. | Pattern Recognition | First Two Moment Recognition | 61 | [
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9.320728936842903,
9.279468596847684
] |
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 | 62 | null |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"7.65",
"1.84",
"18.96"
] | 1.84 | 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 | 63 | [
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] |
Is the given time series likely to be stationary after differencing? | [
"Yes",
"No"
] | 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 | 64 | [
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] |
What is the primary cyclic pattern observed in the time series? | [
"SquareWave",
"No Pattern at all",
"SineWave",
"SawtoothWave"
] | 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 | 65 | [
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] |
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"
] | Yes | 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 | 66 | [
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] |
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"
] | 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 | 67 | null |
Does the given two time series have similar pattern? | [
"No, they have different seasonable pattern",
"Yes, they have similar seasonal pattern"
] | No, they have different seasonable 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 | 68 | null |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"No",
"Yes"
] | No | binary | null | null | 32 | 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"
] | You can try to identify different parts in the time series first, and see if any part is stationary. | Pattern Recognition | Stationarity Detection | 69 | [
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] |
The given time series has a decreasing trend, is it a linear trend or log trend? | [
"Linear",
"Log"
] | Linear | multiple_choice | null | null | 8 | 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 | 70 | [
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Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Additive",
"Multiplicative"
] | Multiplicative | binary | null | null | 59 | 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. | [
"Additive Composition",
"Multiplicative Composition",
"Gaussian White Noise"
] | Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a cyclic component is added with a white noise, the cyclic pattern still remains. When a cyclic component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 71 | [
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] |
Does the given time series exhibit any monotonic increasing trend? | [
"No",
"Yes"
] | 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 | 72 | [
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] |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Remain the same",
"Decrease",
"Increase"
] | Remain the same | multiple-choice | null | null | 19 | 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"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 73 | [
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What type of noise is present in the given time series? | [
"Red Noise",
"Gaussian White Noise",
"No significant noise"
] | No significant noise | multiple_choice | null | null | 62 | medium | 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"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 74 | [
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] |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Remain the same",
"Increase",
"Decrease"
] | Remain the same | multiple-choice | null | null | 17 | 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"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 75 | [
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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 | 76 | null |
The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series in terms of amplitude? | [
"SineWave",
"SquareWave",
"SawtoothWave"
] | SquareWave | multiple-choice | null | null | 20 | 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",
"Amplitude"
] | The cyclic patterns have different period and amplitude. The dominant pattern is the one that has the highest amplitude. Identify the pattern with the highest peak. | Pattern Recognition | Cycle Recognition | 77 | [
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] |
You are given two time series following similar pattern. Both of them have an anomaly. Do they have the same type of anomaly? | [
"Yes, Time series 1 and time series 2 both have flip anomaly",
"No. They have different types of anomalies: cutoff and spikes"
] | No. They have different types of anomalies: cutoff and spikes | binary | [
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] | 76 | 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",
"Spike Anomaly"
] | For each time series, identify the type of anomaly based on the given definitions. Then, check if they have the same type of anomaly. | Anolmaly Detection | General Anomaly Detection | 78 | null |
You are seeing two instances of random walk. Are they likely to have the same variance? | [
"Yes, they have the same variance",
"No, time series 2 has higher variance",
"No, time series 1 has higher 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 | 79 | null |
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 trend reversal | 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 | 80 | [
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] |
Is the given time series likely to be stationary after differencing? | [
"No",
"Yes"
] | Yes | 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 | 81 | [
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] |
Is time series 1 a lagged version of time series 2? | [
"No, time series 2 is a lagged version of time series 1",
"Yes",
"No, they do not share similar pattern"
] | 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 | 82 | null |
The given timeseries is a combination of trend, seasonality and noise. Can you identify the pattern despite the noise? | [
"Noise dominated",
"Yes, Linear Trend and Sine Wave"
] | Noise dominated | multiple_choice | null | null | 13 | 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",
"Gaussian White Noise"
] | Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern. | Pattern Recognition | Trend Recognition | 83 | [
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] |
What is the most likely mean of the given time series? | [
"27.24",
"9.87",
"-16.81"
] | -16.81 | 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 | 84 | [
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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 flip anomaly and time series 2 with speed up/down anomaly",
"Time series 1 with cutoff 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 | 85 | null |
The given time series has square wave pattern. How does its period change from the beginning to the end? | [
"Decrease",
"Increase",
"Remain the same"
] | Decrease | multiple-choice | null | null | 18 | 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. | [
"Square Wave",
"Period"
] | Base on the definition of period, check if the time interval between two peaks remains the same. | Pattern Recognition | Cycle Recognition | 86 | [
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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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] | 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 | 87 | null |
Does the following time series exhibit a mean reversion property? | [
"Yes",
"No"
] | No | binary | null | null | 46 | 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 Reversion"
] | Mean reversion first requires the time series have constant mean. You should check this first. Then, see if the time series tends to revert back to the mean after a shock. | Pattern Recognition | AR/MA recognition | 88 | [
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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 cutoff anomaly and time series 2 with flip anomaly",
"Time series 1 with flip 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 | 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 | 89 | null |
Two time series are given. One has noise and the other does not. Do they have similar pattern? | [
"No, they have different seasonal pattern: Square Wave and Swatooth Wave",
"Yes, they are all Sine Wave"
] | Yes, they are all Sine Wave | binary | [
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] | 82 | 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"
] | 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 | 90 | 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. | [
"6.57",
"1.78",
"0"
] | 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 | 91 | [
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The time series shows a structural break. What is the most likely cause of this break? | [
"Change in variance in underlying distribution",
"Abrupt frequency change",
"Sudden shift in trend direction"
] | Abrupt frequency change | multiple_choice | null | null | 71 | 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",
"Gaussian White Noise",
"Sine Wave"
] | You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options. | Anolmaly Detection | General Anomaly Detection | 92 | [
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] |
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. | [
"2.16",
"14.66",
"0"
] | 14.66 | 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 | 93 | [
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] |
The given timeseries is a combination of trend, seasonality and noise. Can you identify the pattern despite the noise? | [
"Yes, Linear Trend and Sine Wave",
"Noise dominated"
] | Noise dominated | multiple_choice | null | null | 13 | 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",
"Gaussian White Noise"
] | Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern. | Pattern Recognition | Trend Recognition | 94 | [
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] |
The given time series has a decreasing trend, is it a linear trend or log trend? | [
"Log",
"Linear"
] | Log | multiple_choice | null | null | 8 | 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 | 95 | [
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] |
You are given two time series which both have upward trend. Which time series has a higher slope in terms of magnitude? | [
"Time series 2 has higher slope",
"Time series 1 has higher slope"
] | Time series 1 has higher slope | binary | [
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] | 80 | 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",
"Sine Wave",
"Sawtooth Wave"
] | Slope refers to the steepness of the trend. You should check the direction of the trend and the steepness of the trend. If the trend is upward, you should check the magnitude of the slope. | Similarity Analysis | Shape | 96 | null |
The given time series has sine wave pattern. How does its amplitude change from the beginning to the end? | [
"Decrease",
"Remain the same",
"Increase"
] | Remain the same | multiple-choice | null | null | 17 | 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"
] | Base on the definition of amplitude, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 97 | [
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] |
There are two time series given. Is one of them a scaled version of the other? | [
"No, they do not share similar pattern",
"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 | 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 | 98 | null |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"Speedup: the period of cyclic components is different from other parts of the time series and Cutoff: the pattern of time series disappeared for certain point in time and became flat",
"Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time",
"Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time"
] | Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 68 | 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 should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 99 | [
0,
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] |
What is the type of the trend of the given time series? | [
"Exponential",
"No Trend",
"Linear"
] | Exponential | multiple_choice | null | null | 1 | 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"
] | It would be helpful to check if slope of the time series changes over time. | Pattern Recognition | Trend Recognition | 100 | [
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Subsets and Splits