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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"
] | 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 | 701 | null |
Two time series are given. Both of them have a noise component. Do they have the same level (variance) 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 | [
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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 | 702 | null |
What is the direction of the linear trend of the given time series, if any? | [
"Upward",
"Downward",
"No Trend"
] | Upward | multiple_choice | null | null | 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 | 703 | [
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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 1 has higher slope",
"Time series 2 has higher slope"
] | Time series 2 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 | 704 | null |
You are seeing two instances of 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 2 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 | 705 | null |
Is time series 1 a lagged version of time series 2? | [
"No, time series 2 is a lagged version of time series 1",
"No, they do not share similar pattern",
"Yes"
] | Yes | multiple_choice | [
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] | 97 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 706 | 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"
] | No, they have different cyclic components | binary | [
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] | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Square Wave"
] | You should focus on the cyclic components of the time series. Do they have similar patterns aside from the trend? | Similarity Analysis | Shape | 707 | null |
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 | 708 | null |
You are given two time series which both have a trend component. Do they share the same direction of trend (upward or downward)? | [
"Yes, they have the same direction of trend",
"No, they have different direction of trend"
] | Yes, they have the same direction of trend | binary | [
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] | 81 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave"
] | Trend refers to the general direction of the time series. Are the values going up or down? Check this for both time series to see if they have the same direction of trend. | Similarity Analysis | Shape | 709 | null |
What type of noise is present in the given time series? | [
"No significant noise",
"Red Noise",
"Gaussian White Noise"
] | Gaussian White 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 | 710 | [
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] |
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 | null | null | 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 | 711 | [
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] |
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 | [
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] | 83 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Amplitude refers to the height of the peak and the depth of the trough in the cyclic component. You should check the height of the peak and the depth of the trough for both time series. | Similarity Analysis | Shape | 712 | null |
Seasonal stationarity refers to a time series where statistical properties remain constant within seasons but may vary between seasons. Does the time series exhibit seasonal stationarity? | [
"No",
"Yes"
] | No | binary | null | null | 37 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Sine Wave",
"Linear Trend",
"Gaussian White Noise"
] | Determine if the statistical properties of the series are constant within seasons across years. | Pattern Recognition | Stationarity Detection | 713 | [
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] |
What is the primary cyclic pattern observed in the time series? | [
"SawtoothWave",
"SquareWave",
"No Pattern at all",
"SineWave"
] | 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 | 714 | [
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The given time series is a gaussian white noise process. What is the most likely noise level (variance)? | [
"4.66",
"0.35",
"10.81"
] | 10.81 | multiple_choice | null | null | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 715 | [
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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 | 716 | null |
Which of the following time series is more likely to be an MA(1) process? | [
"Time Series 1",
"Time Series 2"
] | Time Series 1 | multiple_choice | [
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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 | 717 | null |
What is the primary cyclic pattern observed in the time series? | [
"SineWave",
"SquareWave",
"No Pattern at all",
"SawtoothWave"
] | SineWave | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 718 | [
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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 increase over time | multiple-choice | null | null | 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 | 719 | [
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] |
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 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 | 720 | [
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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"
] | 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 | 721 | 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? | [
"SawtoothWave",
"SineWave",
"SquareWave"
] | SineWave | 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 | 722 | [
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] |
The time series has a trend and cyclic component added together. Which components are most likely present in the given time series? | [
"Linear trend and sine wave",
"Exponential trend and sine wave",
"No trend and sawtooth wave"
] | No trend and sawtooth wave | multiple-choice | null | null | 26 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Sine Wave",
"Sawtooth Wave",
"Additive Composition"
] | For trend, check if the slope is constant or changes over time. For cyclic component, check the overall shape of the time series. | Pattern Recognition | Cycle Recognition | 723 | [
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] |
The following time series has an anomaly with short term disruption on its pattern. What is the likely pattern of the time series without the anomaly? | [
"Square wave times log trend",
"Sine wave times linear trend",
"Sawtooth wave times linear trend"
] | Sawtooth wave times linear trend | multiple_choice | null | null | 72 | 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",
"Wander Anomaly"
] | Wander anomaly brings short term disruption on the pattern. You should focus on the overall pattern of the time series without the anomaly. | Anolmaly Detection | General Anomaly Detection | 724 | [
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] |
Two time series are given with different cyclic components. Which time series has a higher period of the cyclic component? | [
"Time series 1 has higher period",
"Time series 2 has higher period"
] | Time series 1 has higher period | binary | [
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] | 84 | 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",
"Period"
] | Period refers to the length of one cycle in the cyclic component. You should check the distance between two peaks or two troughs for both time series. | Similarity Analysis | Shape | 725 | null |
What type of trend does the time series exhibit in the latter half? | [
"Exponential",
"No trend",
"Linear"
] | 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 | 726 | [
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] |
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 | [
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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 | 727 | null |
What is the most likely variance of the given time series? | [
"varies across time",
"0.43",
"0"
] | 0.43 | 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 | 728 | [
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] |
Two time series are given. Both of them have a noise component. Do they have the same type of noise? | [
"No, they have different noise: white noise and red noise",
"Yes, they both have Gaussian white noise"
] | Yes, they both have Gaussian white noise | binary | [
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] | 87 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise",
"Additive Composition"
] | When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns. | Similarity Analysis | Shape | 729 | null |
Does the given time series exhibit regime switching? | [
"Yes",
"No"
] | Yes | binary | null | null | 39 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | Identify whether the time series exhibit different patterns over time. | Pattern Recognition | Regime Switching Detection | 730 | [
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] |
The given time series has an increasing trend, is it a linear trend or log trend? | [
"Linear",
"Log"
] | Log | multiple_choice | null | null | 7 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Log Trend"
] | Check if the slope of the time series is constant or changes over time. | Pattern Recognition | Trend Recognition | 731 | [
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] |
You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component? | [
"Time series 1",
"Time series 2"
] | Time series 2 | multiple_choice | [
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1.652802797434234,
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0.632634856910171,
0.046662614195781216,
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2.066573514287039,
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] | 61 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Variance"
] | The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average? | Noise Understanding | Signal to Noise Ratio Understanding | 732 | null |
Is time series 2 a lagged version of time series 1? | [
"No, time series 1 is a lagged version of time series 2",
"Yes",
"No, they do not share similar pattern"
] | No, they do not share similar pattern | multiple_choice | [
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] | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 733 | null |
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 | 734 | [
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Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Multiplicative",
"Additive"
] | Additive | 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 | 735 | [
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Which additive combination of patterns best describes the time series? | [
"SineWave + SquareWave",
"SawtoothWave + SquareWave",
"SineWave + SawtoothWave"
] | SineWave + 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 | 736 | [
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] |
Is time series 2 a lagged version of time series 1? | [
"No, time series 1 is a lagged version of time series 2",
"Yes",
"No, they do not share similar pattern"
] | 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 | 737 | null |
What is the most likely autocorrelation at lag 1 for the given time series? | [
"High positive autocorrelation around 0.8",
"Negative autocorrelation around -0.8",
"No autocorrelation"
] | High positive autocorrelation around 0.8 | multiple_choice | null | null | 45 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. | Pattern Recognition | AR/MA recognition | 738 | [
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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, 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 | 739 | null |
You are given two time series which both have a trend component. Do they share the same direction of trend (upward or downward)? | [
"Yes, they have the same direction of trend",
"No, they have different direction of trend"
] | No, they have different direction of trend | binary | [
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] | 81 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave"
] | Trend refers to the general direction of the time series. Are the values going up or down? Check this for both time series to see if they have the same direction of trend. | Similarity Analysis | Shape | 740 | null |
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 exponential trend and time series 2 has log trend",
"No, time series 1 has linear trend and time series 2 has exponential trend"
] | No, time series 1 has linear trend and time series 2 has exponential trend | multiple_choice | [
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] | 85 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 741 | null |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Linear -> Exponential -> Log",
"Exponential -> Linear -> Log",
"Linear -> Exponential",
"Log"
] | Linear -> Exponential -> Log | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 742 | [
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25.164557674888123,
25.123829825950295,
25.181430117817712,
25.277210687907903
] |
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 2 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 | 743 | 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 | 744 | [
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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? | [
"Random Walk",
"White Noise"
] | 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 | 745 | [
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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 | 746 | [
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Subsets and Splits