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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? | [
"Yes",
"No"
] | Yes | binary | 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 | 401 | [
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Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Multiplicative",
"Additive"
] | Multiplicative | binary | 58 | 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 | 402 | [
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] | null |
The given time series is a square wave. What is the most likely period of the square wave? | [
"30.23",
"15.78",
"57.77"
] | 30.23 | multiple-choice | 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 | 403 | [
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Does the given time series exhibit regime switching? | [
"No",
"Yes"
] | Yes | binary | 40 | 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 | 404 | [
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What is the type of the trend of the given time series? | [
"Exponential",
"Linear",
"No Trend"
] | Linear | multiple_choice | 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 | 405 | [
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] | 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 2 with cutoff anomaly",
"Time series 1 with flip anomaly",
"Time series 1 with speed up/down anomaly"
] | Time series 2 with cutoff anomaly | multiple_choice | 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 | 406 | [
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] |
Two time series are given. Both of them have a noise component. Do they have the same level 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 | 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 | 407 | [
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] |
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 5 to 20",
"Lagging step is between 30 to 45",
"Lagging step is between 60 to 75"
] | Lagging step is between 30 to 45 | multiple_choice | 100 | 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 | 408 | [
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] |
Does the following time series exhibit a mean reversion property? | [
"No",
"Yes"
] | Yes | binary | 47 | 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 | 409 | [
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] | null |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Increase",
"Remain the same",
"Decrease"
] | Remain the same | multiple-choice | 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",
"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 | 410 | [
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] | 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? | [
"Yes",
"No"
] | No | binary | 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 | 411 | [
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] | null |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"No",
"Yes"
] | No | binary | 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 | 412 | [
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] | null |
Despite the noise, does the given two time series have similar pattern? | [
"No, they have different shape",
"Yes, they have similar shape"
] | Yes, they have similar shape | binary | 80 | 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 | 413 | [
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] |
What is the type of the trend of the given time series? | [
"Exponential",
"No Trend",
"Linear"
] | No Trend | multiple_choice | 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 | 414 | [
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] | null |
Is the given time series likely to be stationary after removing the trend? | [
"Yes",
"No"
] | Yes | binary | 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 | 415 | [
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1.5407627515652866,
2.3670057407743843
] | null |
Does the given time series exhibit regime switching? | [
"No",
"Yes"
] | Yes | binary | 40 | 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 | 416 | [
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] | null |
Does any part of the given time series, composed of several concatenated patterns, appear to be stationary? | [
"No",
"Yes"
] | Yes | binary | 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"
] | You can try to identify different parts in the time series first, and see if any part is stationary. | Pattern Recognition | Stationarity Detection | 417 | [
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] | null |
Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Multiplicative",
"Additive"
] | Additive | binary | 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 | 418 | [
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] | null |
What is the direction of the linear trend of the given time series, if any? | [
"Upward",
"No Trend",
"Downward"
] | No Trend | multiple_choice | 4 | 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"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 419 | [
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] | null |
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 remain the same over time | multiple-choice | 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 | 420 | [
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] | null |
What is the most likely mean of the given time series? | [
"22.21",
"5.73",
"-13.6"
] | 5.73 | multiple_choice | 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 | 421 | [
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] | null |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"No, they are not granger causality",
"Yes, time series 1 granger causes time series 2"
] | Yes, time series 1 granger causes time series 2 | binary | 103 | 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 | 422 | [
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] |
What is the most likely variance of the given time series? | [
"1",
"varies across time",
"0.53"
] | 0.53 | multiple_choice | 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 | 423 | [
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] | null |
The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series? | [
"SawtoothWave",
"SquareWave",
"SineWave"
] | SawtoothWave | multiple-choice | 21 | 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 | 424 | [
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] | 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 5 to 20",
"Lagging step is between 60 to 75"
] | Lagging step is between 60 to 75 | multiple_choice | 100 | 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 | 425 | [
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] |
What is the direction of the linear trend of the given time series, if any? | [
"No Trend",
"Upward",
"Downward"
] | Upward | multiple_choice | 4 | 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"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 426 | [
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] | null |
The given time series has a cycle component and a trend component. Is it an additive or multiplicative model? | [
"Additive",
"Multiplicative"
] | Multiplicative | multiple_choice | 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 | 427 | [
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] | null |
You are seeing two time series that are random walk. Are they likely to have the same variance? | [
"No, time series 2 has higher variance",
"Yes, they have the same variance",
"No, time series 1 has higher variance"
] | No, time series 1 has higher variance | multiple_choice | 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 | 428 | [
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] |
What is the primary cyclic pattern observed in the time series? | [
"SquareWave",
"SawtoothWave",
"No Pattern at all",
"SineWave"
] | No Pattern at all | multiple-choice | 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 | 429 | [
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] | null |
The given time series has square wave pattern. How does its period change from the beginning to the end? | [
"Remain the same",
"Decrease",
"Increase"
] | Remain the same | multiple-choice | 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 | 430 | [
-0.11970550048264106,
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] | null |
The given time series is a random walk process. What is the most likely noise level? | [
"7.74",
"1.72",
"4.6"
] | 7.74 | multiple_choice | 55 | 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 | 431 | [
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] | null |
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 | 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 | 432 | [
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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 5 to 20",
"Lagging step is between 30 to 45",
"Lagging step is between 60 to 75"
] | Lagging step is between 60 to 75 | multiple_choice | 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 | 433 | [
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] |
Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity? | [
"Yes",
"No, the variance is different overtime",
"No, the mean is different overtime"
] | No, the variance is different overtime | multiple_choice | 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 | 434 | [
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] | null |
Based on the given time series, how many different regimes are there? | [
"4",
"3",
"1"
] | 1 | multiple_choice | 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 | 435 | [
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] | null |
Does the given two time series have similar pattern? | [
"Yes, they have similar shape",
"No, they have different shape"
] | No, they have different shape | binary | 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 | 436 | [
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] |
Is the given time series stationary? | [
"No",
"Yes"
] | No | binary | 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"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 437 | [
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] | null |
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component? | [
"Linear",
"Exponential",
"Log"
] | Exponential | multiple_choice | 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 | 438 | [
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12.377237681836315,
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] | 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 | 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. | [
"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 | 439 | [
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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",
"Log",
"Exponential -> Linear -> Log"
] | Exponential -> Linear -> Log | multiple_choice | 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 | 440 | [
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] | null |
Which additive combination of patterns best describes the time series? | [
"SineWave + SquareWave",
"SawtoothWave + SquareWave",
"SineWave + SawtoothWave"
] | SawtoothWave + SquareWave | multiple-choice | 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 | 441 | [
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] | null |
What is the most likely linear trend coefficient of the given time series? | [
"2.38",
"0",
"9.9"
] | 0 | multiple_choice | 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 | 442 | [
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2.5394280895003107,
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] | 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"
] | No, they do not share similar pattern | binary | 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. | [
"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 | 443 | [
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] |
What is the type of the trend of the given time series? | [
"Exponential",
"No Trend",
"Linear"
] | Linear | multiple_choice | 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 | 444 | [
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] | null |
Is the two time series lagged version of each other despite minor noise? | [
"No, they are not lagged versions at all",
"Yes, they are lagged versions"
] | Yes, they are lagged versions | binary | 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 | 445 | [
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] |
Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude decrease over time",
"Amplitude increase over time",
"Amplitude remain the same over time"
] | Amplitude increase over time | multiple-choice | 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 | 446 | [
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] | null |
Which of the following best describe the cycle pattern in the given time series? | [
"Period increase over time",
"Period decrease over time",
"Period remain the same over time"
] | Period decrease over time | multiple-choice | 29 | 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",
"Period"
] | Check the time interval between two peaks, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 447 | [
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] | null |
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 1",
"Time Series 2"
] | Time Series 2 | multiple_choice | 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 | 448 | [
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The following time series has a noise component. Is it a white noise or random walk? | [
"Random Walk",
"White Noise"
] | White Noise | binary | 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 | 449 | [
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] | null |
Does the trend of the time series change direction? | [
"No",
"Yes"
] | No | binary | 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 | 450 | [
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] | null |
The following time series has an anomaly with random large fluctuations. What is the likely pattern of the time series without the anomaly? | [
"Square wave with log trend",
"Sawtooth wave with linear trend",
"Sine wave with linear trend"
] | Sine wave with linear trend | multiple_choice | 66 | 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",
"Spike Anomaly"
] | Spikes anomaly bring constant large random fluctuations. Can you check the place where the spikes disappear and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 451 | [
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] | null |
Does the following time series exhibit a mean reversion property? | [
"Yes",
"No"
] | No | binary | 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 | 452 | [
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] | 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? | [
"No, they have different cyclic components",
"Yes, they share a similar pattern"
] | Yes, they share a similar pattern | binary | 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 | 453 | [
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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 | 104 | 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 | 454 | [
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Which additive combination of patterns best describes the time series? | [
"SineWave + SawtoothWave",
"SineWave + SquareWave",
"SawtoothWave + SquareWave"
] | SawtoothWave + SquareWave | multiple-choice | 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",
"Square Wave",
"Sawtooth Wave",
"Additive Composition"
] | Imagine the shape of the time series as addition of two different patterns. | Pattern Recognition | Cycle Recognition | 455 | [
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] | null |
Is time series 2 a lagged version of time series 1? | [
"No, they do not share similar pattern",
"No, time series 1 is a lagged version of time series 2",
"Yes"
] | Yes | multiple_choice | 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 | 456 | [
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] |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Log",
"Exponential -> Linear -> Log",
"Linear -> Exponential",
"Linear -> Exponential -> Log"
] | Log | multiple_choice | 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 | 457 | [
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] | null |
What is the type of the trend of the given time series? | [
"No Trend",
"Exponential",
"Linear"
] | No Trend | multiple_choice | 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 | 458 | [
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] | 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"
] | Yes, they have the same underlying distribution | binary | 94 | 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 | 459 | [
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] |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"18.21",
"2.68",
"4.21"
] | 4.21 | multiple-choice | 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 | 460 | [
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] | null |
Is time series 1 a lagged version of time series 2? | [
"Yes",
"No, they do not share similar pattern",
"No, time series 2 is a lagged version of time series 1"
] | Yes | multiple_choice | 99 | 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 | 461 | [
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What is the most likely autocorrelation at lag 1 for the given time series? | [
"No autocorrelation",
"Negative autocorrelation",
"High positive autocorrelation"
] | High positive autocorrelation | multiple_choice | 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. | [
"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 | 462 | [
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] | null |
What type of noise is present in the given time series? | [
"Gaussian White Noise",
"Red Noise",
"No significant noise"
] | No significant noise | multiple_choice | 63 | 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 | 463 | [
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] | null |
Are there any granger causality between the two time series? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causality",
"Yes, time series 2 granger causes time series 1"
] | No, they are not granger causality | binary | 103 | 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 | 464 | [
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The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series? | [
"SawtoothWave",
"SquareWave",
"SineWave"
] | SquareWave | multiple-choice | 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 | 465 | [
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] | null |
What is the most likely linear trend coefficient of the given time series? | [
"0",
"4.28",
"8.31"
] | 4.28 | multiple_choice | 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 | 466 | [
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] | null |
What is the primary cyclic pattern observed in the time series? | [
"SawtoothWave",
"SquareWave",
"No Pattern at all",
"SineWave"
] | SquareWave | multiple-choice | 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 | 467 | [
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] | null |
You are seeing two time series that are random walk. Are they likely to have the same variance? | [
"No, time series 1 has higher variance",
"No, time series 2 has higher variance",
"Yes, they have the same variance"
] | No, time series 2 has higher variance | multiple_choice | 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 | 468 | [
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Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"1",
"4",
"2"
] | 4 | multiple_choice | 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 | 469 | [
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] | null |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"6.34",
"2.2",
"18.98"
] | 2.2 | multiple-choice | 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 | 470 | [
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] | null |
What is the primary cyclic pattern observed in the time series? | [
"SquareWave",
"No Pattern at all",
"SawtoothWave",
"SineWave"
] | No Pattern at all | multiple-choice | 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 | 471 | [
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] | null |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"18.21",
"2.85",
"5.99"
] | 2.85 | multiple-choice | 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. | [
"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 | 472 | [
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] | null |
Is the given time series likely to be stationary after differencing? | [
"No",
"Yes"
] | No | binary | 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 | 473 | [
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] | 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"
] | Yes, they have the same underlying distribution | binary | 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 | 474 | [
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] |
What is the direction of the linear trend of the given time series, if any? | [
"Downward",
"Upward",
"No Trend"
] | Downward | multiple_choice | 4 | 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"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 475 | [
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] | null |
Which of the following best describe the cycle pattern in the given time series? | [
"Period decrease over time",
"Period increase over time",
"Period remain the same over time"
] | Period decrease over time | multiple-choice | 30 | 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",
"Period"
] | Check the time interval between two peaks, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 476 | [
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] | null |
What is the most likely linear trend coefficient of the given time series? | [
"4.92",
"9.82",
"0"
] | 4.92 | multiple_choice | 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 | 477 | [
-0.44429776780675356,
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5.089106394214815,
4.100328065584422,
5.356458156678102
] | 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",
"Exponential trend and square wave",
"Linear trend and sine wave"
] | Linear trend and sine wave | multiple_choice | 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",
"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 | 478 | [
0,
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] | null |
The given time series is a square wave. What is the most likely period of the square wave? | [
"33.49",
"13.34",
"51.29"
] | 13.34 | multiple-choice | 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 | 479 | [
0.06777665661808034,
1.0330424865693308,
1.0306741709924394,
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] | null |
Two time series are given. Both of them have a noise component. Do they have the same level of noise? | [
"No, they have different level of noise",
"Yes, they both have the same level of noise"
] | No, they have different level of noise | binary | 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. | [
"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 | 480 | [
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] |
The following time series has an anomaly with random large fluctuations. What is the likely pattern of the time series without the anomaly? | [
"Square wave with log trend",
"Sine wave with linear trend",
"Sawtooth wave with linear trend"
] | Sawtooth wave with linear trend | multiple_choice | 66 | 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",
"Spike Anomaly"
] | Spikes anomaly bring constant large random fluctuations. Can you check the place where the spikes disappear and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 481 | [
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] | null |
In which part of the time series does the anomaly occur? | [
"Middle",
"Beginning",
"End"
] | End | multiple_choice | 77 | 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. | [
"Sine Wave",
"Linear Trend",
"Spike Anomaly",
"Cutoff Anomaly",
"Wander Anomaly"
] | Identify where in the time series sequence the unusual pattern or disruption occurs. | Anolmaly Detection | General Anomaly Detection | 482 | [
0.04967141530112327,
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] | null |
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 causality"
] | Yes, time series 1 granger causes time series 2 | binary | 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 | 483 | [
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] |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is flipped at certain point in time",
"Yes, it's pattern is distorted by random spikes",
"No"
] | No | binary | 63 | 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. | [
"Flip Anomaly",
"Spike 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 | 484 | [
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] | null |
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 | 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 | 485 | [
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] |
The given time series has multiple cycle patterns with same amplitude and period. How are they combined together? | [
"Multiplicative",
"Additive"
] | Additive | multiple-choice | 27 | 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",
"Multiplicative Composition"
] | For additive composition, the patterns are added together. This changes amplitude. For multiplicative composition, the overall shape of the time series might be distorted with cyclic patterns unobservable. | Pattern Recognition | Cycle Recognition | 486 | [
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] | null |
One type of noise in time series is white noise. Is the given time series noisy based on your understanding of white noise? | [
"No",
"Yes"
] | Yes | binary | 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. | [
"Gaussian White 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. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 487 | [
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] | 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 2 has higher amplitude | binary | 84 | 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 | 488 | [
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] |
Two time series are given. Both of them have a noise component. Do they have the same level of noise? | [
"No, they have different level of noise",
"Yes, they both have the same level of noise"
] | No, they have different level of noise | binary | 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 | 489 | [
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] |
Is the given time series likely to have an anomaly? | [
"Yes, it's pattern is flipped at certain point in time",
"No",
"Yes, it's pattern is distorted by random spikes"
] | Yes, it's pattern is flipped at certain point in time | binary | 63 | 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. | [
"Flip Anomaly",
"Spike 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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] | null |
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 increase over time | multiple-choice | 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 | 491 | [
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] | null |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"31.01",
"50.69",
"19.05"
] | 31.01 | multiple-choice | 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. | [
"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 | 492 | [
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] | null |
What is the direction of the linear trend of the given time series, if any? | [
"Downward",
"Upward",
"No Trend"
] | Downward | multiple_choice | 4 | 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"
] | Check if the time series values increase or decrease over time. | Pattern Recognition | Trend Recognition | 493 | [
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] | null |
The following time series has a noise component. Is it a white noise or random walk? | [
"White Noise",
"Random Walk"
] | Random Walk | binary | 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 | 494 | [
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] | null |
Two time series are given. One has noise and the other does not. Do they have similar pattern? | [
"Yes, they have similar pattern",
"No, they have different pattern"
] | Yes, they have similar pattern | binary | 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 | 495 | [
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] |
The given time series has square wave pattern. How does its period change from the beginning to the end? | [
"Remain the same",
"Increase",
"Decrease"
] | Decrease | multiple-choice | 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 | 496 | [
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] | null |
Despite the noise, does the given two time series have similar pattern? | [
"No, they have different shape",
"Yes, they have similar shape"
] | No, they have different shape | binary | 80 | 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 | 497 | [
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You are given two time series with same underlying pattern but different noise level. Which time series has higher magnitude of noise? | [
"Time series 1",
"Time series 2"
] | Time series 2 | multiple_choice | 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. | [
"Sine Wave",
"Exponential Trend",
"Gaussian White Noise",
"Variance"
] | When the noise level is high, it can distort the pattern in the time series. Both time series have the same underlying pattern, but different noise level. To tell which time series has higher noise level, you should check the degree of distortion of the time series pattern. | Noise Understanding | Signal to Noise Ratio Understanding | 498 | [
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] |
Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity? | [
"Yes",
"No, the variance is different overtime",
"No, the mean is different overtime"
] | Yes | multiple_choice | 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 | 499 | [
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] | 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 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"
] | Time series 1 with cutoff anomaly and time series 2 with flip anomaly | multiple_choice | 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. | [
"Sine Wave",
"Sawtooth Wave",
"Linear Trend",
"Log Trend",
"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 | 500 | [
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