Datasets:
input stringlengths 29 325 | question_format stringclasses 3
values | answer stringlengths 1 573 | timeseries listlengths 1 6 |
|---|---|---|---|
The data points <ts><ts/> exhibit a repeating pattern. True or false? | true/false | F | [
[
12.75,
14.19,
14.12,
13.59,
14.59,
15.94,
16.81,
17.06,
18.19,
19.44,
19.28,
17.56,
15.81,
15.09,
14.38,
12.59,
11.66,
12.72,
13.94,
14.19,
13.34
]
] |
Identify any structural breaks in the following points <ts><ts/>. | open_ended_question | A structural break occurs between 15.12 and 17.81, representing a sharp increase that doesn't align with the previous adjacent data points. Such changes might signal a shift in the structure of the dataset, perhaps due to an external factor or influence. | [
[
11.78,
12.38,
13.5,
14.03,
14.19,
13.44,
13.19,
12.34,
12.38,
12.75,
14.19,
15.12,
17.81,
19.53,
21.97,
23.97,
24.72,
23.28,
20.34,
17.84,
16.41,
14.47,
14.66,
14.44
]
] |
Summarize the behavior of the following time series data points: <ts><ts/>. | open_ended_question | The series has intermittent spikes embedded in generally low levels, with occasional increases like 45.0 and 32.0, before reverting to lower values. It reflects variability but lacks a clear trend or cyclical pattern. | [
[
13,
3,
8,
22,
27,
45,
32,
21,
2,
0,
14,
7,
5,
1,
1,
2,
2,
4,
1,
2,
7,
1,
1,
15
]
] |
Identify a potential structural break in this sequence: <ts><ts/>. A] Between points 7 and 8 B] Between points 12 and 13 C] Between points 19 and 20 | multiple_choice | A | [
[
1.41,
0.19,
-1.31,
-1.7,
-1.46,
-0.2,
0.24,
-0.13,
-0.94,
-1.46,
-0.9,
0.17,
0.31,
0.09,
-0.17,
-0.24,
-0.32,
-0.41,
0.98,
1.4,
0.88,
-0.2,
0.06,
0.09
]
] |
Does the following series exhibit seasonality? <ts><ts/>.
Options: a] Yes, it repeats a pattern. b] No, it does not repeat a pattern. | multiple_choice | B | [
[
1.14,
1.12,
1.12,
1.07,
1.03,
1.01,
1.01,
1.07,
1.15,
1.22,
1.34,
1.46,
1.6,
1.8,
1.97,
1.91,
1.72,
1.65,
1.66,
1.62,
1.47,
1.28,
1.15,
1.02
]
] |
Based on the time series data points <ts><ts/>, summarize the movement of this data. | open_ended_question | The data begins with slight fluctuations around 0.4, decreases to a low of 0.21, then resumes an upward movement reaching a peak at 0.91. The summary suggests a local minimum followed by a strong upward trend. | [
[
0.44,
0.41,
0.44,
0.48,
0.49,
0.42,
0.27,
0.21,
0.22,
0.14,
0.54,
0.58,
0.62,
0.65,
0.7,
0.76,
0.82,
0.86,
0.85,
0.82,
0.81,
0.85,
0.89,
0.91
]
] |
Does the sequence <ts><ts/> show evidence of a structural break? a] Yes b] No c] Uncertain | multiple_choice | A | [
[
217,
232,
242,
247.5,
249.5,
248,
242.5,
233.5,
223.5,
211,
198.5,
184,
168,
152,
136,
122,
108,
92.5,
75,
50.5,
19,
-20,
-68.5,
-117
]
] |
Calculate the mean and standard deviation for these data points: <ts><ts/>. | open_ended_question | Mean = -0.115; Standard deviation = approximately 0.042. Calculated using mean = (sum of all values) / n and standard deviation formula. | [
[
-0.15,
-0.16,
-0.16,
-0.17,
-0.18,
-0.18,
-0.17,
-0.16,
-0.15,
-0.14,
-0.13,
-0.13,
-0.12,
-0.11,
-0.1,
-0.09,
-0.08,
-0.07,
-0.06,
-0.05,
-0.04,
-0.03,
-0.02,
-0.02
]
] |
Examine the time series data <ts><ts/> and summarize the overall movement trend in this data. | open_ended_question | The data exhibits an initial steady increase, reaching a peak at 80.0, followed by a general decline towards the later values. The sequence reflects some minor fluctuations but trends down after reaching a peak. | [
[
34,
34,
51,
40,
38,
52,
53,
51,
66,
63,
65,
51,
54,
80,
69,
50,
33,
42,
54,
23,
25,
26,
30,
29
]
] |
Given the series <ts><ts/>, identify if there are any anomalies. A] Yes, the first value is an anomaly B] Yes, the twelfth value is an anomaly C] No, there are no anomalies | multiple_choice | C | [
[
-2907.15,
-2903.63,
-2893.19,
-2889.54,
-2887.98,
-2891.63,
-2892.91,
-2888.73,
-2885.35,
-2884.49,
-2894.73,
-2898.44,
-2884.21,
-2877.9,
-2884.71,
-2895.44,
-2901.79,
-2897.63,
-2896.14,
-2897.32,
-2895.08,
-2894.67,
-28... |
Identify any anomalies in the time series data: <ts><ts/>. Explain the reasoning. | open_ended_question | The data point -11091.42 deviates minimally from the surrounding values, which mostly range between -11089.42 and -11103.87. There are no strong outliers or anomalies; thus, the series is fairly consistent without extreme deviations. | [
[
-11088.87,
-11103.87,
-11097.37,
-11091.42,
-11089.42,
-11091.59,
-11091.42,
-11092.96,
-11098.13,
-11100.38,
-11097.73,
-11094.77,
-11098.4,
-11101.54,
-11102.72
]
] |
Calculate the mean and standard deviation of the data set <ts><ts/>. | open_ended_question | The mean of the data set is approximately -0.32 and the standard deviation is around 0.45. Reasoning: The mean is calculated by summing all values and dividing by their count. Standard deviation is the square root of the variance, where variance is found by averaging the squared differences from the mean. | [
[
-0.68,
-0.51,
-0.51,
-0.74,
-0.85,
-0.77,
-0.58,
-0.4,
-0.22,
-0.05,
-0.13,
-0.44,
-0.56,
-0.31,
-0.03,
-0.01,
-0.23,
-0.34,
-0.01,
0.51,
0.52,
-0.08,
-0.64,
-0.59
]
] |
Analyze the data points <ts><ts/> and describe the general trend. | open_ended_question | The trend is non-linear with sudden jumps and decreases. Initially, the trend stays low with minor increases, then jumps to a high with the 33.0 point and fluctuates drastically, showing erratic behavior without a consistent trend. | [
[
2,
4,
0,
3,
33,
21,
24,
2,
2,
9,
1,
3,
2,
16,
21,
18,
2,
0,
3,
6,
2,
4,
1,
7
]
] |
Identify any structural breaks within the data points <ts><ts/>. Explain. | open_ended_question | A structural break appears to occur between -0.57 and -0.04, where there is a transition from steadily negative values to increasing positive values. This suggests a change in the underlying process generating the data. | [
[
-0.89,
-0.92,
-0.94,
-1.17,
-1.19,
-1.31,
-0.88,
-0.66,
-1.15,
-1.07,
-0.93,
-0.57,
-0.04,
0.15,
0.51,
0.39,
0.41,
1,
1.09,
1.09,
1.02,
0.92,
1.04,
1.1
]
] |
Does the section <ts><ts/> exhibit characteristics of seasonality? A] Yes B] No | multiple_choice | B | [
[
-20744.55,
-20739.16,
-20736.46,
-20739.45,
-20741.73,
-20743.95,
-20744.29,
-20743.37,
-20744.76,
-20742.91,
-20739.14,
-20739.76
]
] |
Identify any cyclical patterns in the data series <ts><ts/>. | open_ended_question | The series shows a slight cyclic pattern, with values peaking at 1.11 and then decreasing, resembling a cycle, although the change is not pronounced enough for a strong cyclical argument. The pattern is suggestive of cyclicality but not definitive. | [
[
0.77,
0.8,
0.82,
0.83,
0.84,
0.85,
0.85,
0.85,
0.86,
0.89,
0.92,
0.95,
0.98,
1,
1.03,
1.06,
1.09,
1.11,
1.1,
1.07,
1.03,
1.02,
1.01,
1.02
]
] |
Analyze the sequence <ts><ts/> and describe any patterns or unique characteristics you observe. | open_ended_question | The sequence displays an initial upward trend peaking around 43.88, followed by a consistent decline towards the end. This suggests an overall up-and-down behavior indicative of a cycle. | [
[
32.38,
33.16,
33.69,
34.78,
34.72,
35.56,
37.06,
39.5,
41.25,
43.12,
43.69,
43.88,
42.69,
40.81,
37.5,
34.94,
31.31,
29.47,
28.38,
27.22,
26.16,
26.22,
26.53,
26.62
]
] |
Analyze the presence of any cyclical patterns within the data points <ts><ts/>. | open_ended_question | There is evidence of a cyclical pattern with alternating increases and decreases. The data follows a pattern where it periodically rises and falls, suggesting possible cycles of growth and decline. | [
[
8.08,
7.32,
6.14,
7.14,
8.61,
10.84,
4.9,
13.95,
11.1,
6.81,
6.6,
9.25,
4.03,
8.55,
7.84,
5.23,
3.11,
6.63,
8.61,
10.84,
4.9,
13.95,
11.1,
6.81
]
] |
Summarize the pattern observed in the time series data from the points <ts><ts/>. | open_ended_question | This time series shows a fluctuating pattern with apparent highs and lows, where data points swing between positive and negative values, largely highlighting volatility. | [
[
1.02,
-0.22,
1.36,
-0.18,
0.37,
0.33,
-0.42,
0.93,
-0.37,
0.75,
-0.34,
0.07,
-0.14,
-0.44,
0.22,
-0.63,
0.22,
-0.75,
-0.2,
-0.98,
-0.81,
-0.7,
-1.32,
-0.46
]
] |
Summarize the movement observed in these data points <ts><ts/>. | open_ended_question | The data initially shows an upward trend, then stabilizes at a higher level, before exhibiting a downward trend. This suggests early growth, a peak, and eventual decline. | [
[
5.88,
6.81,
8.09,
8.31,
9.34,
11.75,
13.81,
13.47,
11.97,
11.59,
11.81,
11.44,
11.06,
12.31,
20.47,
21.31,
23.31,
24.44,
21.81,
17.81,
15.44,
14,
11.03,
8.44
]
] |
Based on the data points <ts><ts/>, discuss the volatility of the series. | open_ended_question | The series shows moderate volatility with fluctuating values. Notably, numbers range from a low of 49.0 to a high of 80.0, meaning there's significant variation when comparing the maximum and minimum values in the dataset. | [
[
57,
65,
53,
65,
73,
76,
80,
72,
64,
49,
62,
58
]
] |
In the selected data points <ts><ts/>, a structural break is present. True or false? | true/false | T | [
[
0.47,
0.47,
0.47,
0.48,
0.48,
0.48,
0.48,
0.49,
0.49,
0.49,
0.5,
0.5,
0.5,
0.51,
0.51,
0.52,
0.52,
0.53,
0.53,
0.53,
0.54,
0.54,
0.54,
0.54
]
] |
Consider the time series points <ts><ts/>. Is there evidence of seasonality in this time series? A] Yes, there is clear seasonality. B] No, there is no seasonality evident. C] There are potential but not clear seasonal patterns. D] Seasonality is masked by other patterns. | multiple_choice | B | [
[
0.58,
0.59,
0.6,
0.61,
0.61,
0.61,
0.61,
0.6,
0.6,
0.6,
0.59,
0.58,
0.58,
0.57,
0.56,
0.55,
0.54,
0.53,
0.51,
0.5,
0.49,
0.48,
0.46,
0.45
]
] |
Assess the volatility of the series <ts><ts/>. | open_ended_question | The series shows high volatility. There are noticeable fluctuations where values move from highs of around 1.61 to lows of -1.8, indicating significant variability and swings within the series. | [
[
1.53,
1.61,
1.54,
1.34,
1.19,
1.12,
1.03,
0.95,
1.02,
1.21,
1.24,
1.05,
0.79,
0.59,
0.6,
0.71,
0.57,
-0.18,
-1.32,
-1.8,
-1.03,
0.33,
0.96,
0.33
]
] |
Which of the following sequences best represents a cyclical pattern? A] <ts><ts/>, B] <ts><ts/> | multiple_choice | A | [
[
0.38,
0.38,
0.37,
0.36,
0.35,
0.35,
0.34,
0.33,
0.32,
0.31,
0.31,
0.3,
0.29,
0.29,
0.28,
0.28,
0.27,
0.29,
0.31,
0.34,
0.38,
0.43,
0.47,
0.52
],
[
0.38,
0.37,
0.36,
0.35,
0.34,
0.33,
0.3... |
The data points <ts><ts/> suggest a trend reversal. True or False? | true/false | T | [
[
0.41,
0.41,
0.41,
0.41,
0.4,
0.4,
0.4,
0.41,
0.42,
0.44,
0.46,
0.48,
0.5,
0.53,
0.55,
0.56,
0.58,
0.59,
0.6,
0.61,
0.6,
0.6,
0.6,
0.6
]
] |
What is the average value of the dataset <ts><ts/>? a] 40.45 b] 42.71 c] 44.23 d] 45.89 | multiple_choice | B | [
[
18.53,
19.06,
18.03,
19.59,
55.19,
55.62,
55.59,
54.06,
53.91,
51.94,
50.5,
50.22,
48.94,
48.91,
50.16,
48.84,
49.31,
50.91,
53.06,
54.44,
30.66,
31.09,
31.56,
31.5
]
] |
Identify any structural break in the sequence <ts><ts/>. | open_ended_question | A structural break appears at the value 46.0, where the sequence shifts from low single-digit numbers to relatively high and irregular numbers. This indicates a possible change in the underlying process generating the data. | [
[
4,
1,
7,
1,
2,
10,
3,
3,
4,
4,
23,
6,
5,
1,
46,
26,
3,
0,
3,
6,
0,
3,
1,
3
]
] |
What is the mean of these data points? <ts><ts/>. A] -0.01 B] 0.01 C] 0.02 D] -0.02 | multiple_choice | B | [
[
0.18,
-0.21,
-0.02,
-0.05,
-0.1,
-0.03,
-0.09,
0,
-0.12,
-0.01,
-0.03,
-0.07,
0.07,
-0.04,
-0.02,
0.04,
-0.01,
0.07,
-0.01,
0.08,
-0.03,
0.15,
-0.11,
0.16
]
] |
Examine the data points <ts><ts/> and describe the overall trend observed in the data. | open_ended_question | The data shows an initial upward trend peaking at 0.29, followed by a decline. The data initially increases, reaching a peak around 0.29, then it starts to decrease, ending at -0.63. | [
[
0.16,
0.18,
0.23,
0.24,
0.26,
0.27,
0.27,
0.28,
0.29,
0.27,
0.27,
0.25,
0.24,
0.23,
0.23,
0.23,
0.22,
0.18,
0.14,
0.05,
-0.04,
-0.17,
-0.36,
-0.63
]
] |
Examine the data points <ts><ts/>, and determine if there appears to be any seasonality pattern present. | open_ended_question | There doesn't appear to be clear seasonality. The data exhibits periods of increase and decrease, but these patterns are more indicative of trends with no repeated periodic ups and downs at consistent intervals. | [
[
-1265.3,
-1267.56,
-1272.92,
-1273.72,
-1275.25,
-1277.68,
-1275.07,
-1272.98,
-1272.56,
-1273.12,
-1269.19,
-1269.74,
-1271.77,
-1270.69,
-1271.56,
-1266.42,
-1261.57,
-1259.38,
-1256.35,
-1255.06,
-1253.32,
-1247.77,
-12... |
Given the time series data <ts><ts/>, describe any cyclical patterns you observe. | open_ended_question | The data does show signs of cyclical patterns where there is a rise in the middle toward positive values and then a fall again towards negative values, suggesting a medium-term cycle. The rise from -7.47 to 6.59 and subsequent dips represent cycles of increased and decreased values. | [
[
-7.34,
-7.47,
-7.94,
-7.78,
-6.34,
-4.25,
-3.56,
-3.09,
-2.28,
-1.31,
-1.72,
-3.09,
-4.06,
-4.53,
-5.34,
-6.84,
-8.5,
-9.34,
-5.38,
-3.88,
-1.66,
0.75,
3.69,
6.59
]
] |
The time series data <ts><ts/> exhibits strong seasonality. True or False? | true/false | F | [
[
43,
7,
46,
16,
36,
10,
41,
24,
42,
25,
18,
23,
17,
13,
8,
38,
13,
20,
8,
32,
17,
17,
10,
16
]
] |
Identify any anomalies in the data points <ts><ts/>. A] 666.06, B] 671.87, C] 671.47, D] No anomalies | multiple_choice | B | [
[
654.57,
656.41,
666.06,
671.87,
669.87,
671.47,
666.55,
661.45,
662.67,
659.89,
659.24,
660.85,
658.75,
657.75,
657.34,
655.07,
656.93,
657.57,
655.38,
651.92,
645.92,
640.63,
639.42,
645.26
]
] |
What are the key peaks in the series <ts><ts/>? | open_ended_question | The key peaks occur at the beginning with values around 33.53 and another around 27.22. These peaks are followed by a general downward trend indicating fluctuations around these peaks. | [
[
33.53,
33.03,
30.5,
28.44,
28.41,
28,
25.69,
23.25,
23.66,
24.66,
23.59,
22.22,
22.81,
24.75,
25.16,
24.53,
24.5,
26.22,
26.56,
25.38,
24.28,
25.56,
27.22,
26.94
]
] |
A structural break is present in the series <ts><ts/>. | true/false | T | [
[
-33,
-20,
0,
15,
33,
48.5,
58,
67,
75.5,
82.5,
85,
84.5,
81,
74,
65,
53.5,
40.5,
22.5,
-3.5,
-33,
-66,
-115,
-161,
-201.5
]
] |
Which data point in the series: <ts><ts/> appears to be an anomaly? Options: a] 3.44, b] -5.03, c] -10.47, d] 1.0 | multiple_choice | A | [
[
-2.75,
-2.62,
-3.97,
-5.03,
-5.03,
-5.31,
-6.97,
-8.56,
-8.84,
-8.81,
-9.69,
-10.47,
-9.47,
-7.81,
-7.06,
-7.38,
-7.34,
3.44,
4.09,
2.06,
-0.59,
-0.38,
0.91,
1
]
] |
Identify any anomalies present in the time series data <ts><ts/>. | open_ended_question | The data point 24.69 is an anomaly, as it significantly deviates from the rest of the negative values in the time series, indicating an abrupt change in the data pattern. | [
[
-15.09,
-16.38,
-18.34,
-19.72,
-21.44,
-22.22,
-25.31,
-27.75,
-31.03,
-34.59,
-38.59,
-42.44,
-44.19,
-45.78,
-45.59,
-44.84,
-44.19,
-42.84,
-42.38,
-42.19,
-42.03,
-42.34,
-42.03,
24.69
]
] |
Identify any anomalies in the data points <ts><ts/>. | open_ended_question | There are no significant anomalies in this set of data points. The data represents a decreasing trend without any sudden spikes or drops that would suggest an anomaly. | [
[
0.79,
0.79,
0.78,
0.77,
0.75,
0.73,
0.71,
0.68,
0.65,
0.63,
0.6,
0.57,
0.55,
0.53,
0.51,
0.49,
0.48,
0.46,
0.45,
0.44,
0.43,
0.42,
0.42,
0.41
]
] |
Assess the level of volatility in the time series values <ts><ts/>. | open_ended_question | The data exhibits a high level of volatility, evidenced by large spikes and drops throughout the dataset, such as moving from 9.0 to 20.0, then down to 7.0 and fluctuations between lower single-digits and peaks around 15.0. | [
[
9,
20,
7,
11,
12,
7,
13,
13,
2,
12,
7,
9,
10,
14,
15,
3,
5,
7,
8,
4,
2,
15,
9,
5
]
] |
Which segment shows the highest volatility in the data points <ts><ts/>? A] -0.14 to -0.08 B] -0.08 to -0.05 C] -0.05 to -0.02 D] -0.02 to -0.08 | multiple_choice | A | [
[
-0.14,
-0.12,
-0.1,
-0.09,
-0.08,
-0.07,
-0.05,
-0.04,
-0.03,
-0.03,
-0.02,
-0.03,
-0.03,
-0.04,
-0.06,
-0.05,
-0.06,
-0.09,
-0.1,
-0.09,
-0.09,
-0.08,
-0.08,
-0.08
]
] |
What is the average of the time series data points <ts><ts/>? | open_ended_question | Average = (22.16 + 23.53 + 24.28 + 24.06 + 24.06 + 23.47 + 21.31 + 19.94 + 18.69 + 16.53 + 15.19 + 14.38 + 13.69 + 13.62 + 13.56 + 13.19 + 12.88 + 12.22 + 9.69 + 10.44 + 8.88 + 9.19 + 10.03 + 11.22) / 24 = 16.50. The mean provides a central value around which the other data points are distributed. | [
[
22.16,
23.53,
24.28,
24.06,
24.06,
23.47,
21.31,
19.94,
18.69,
16.53,
15.19,
14.38,
13.69,
13.62,
13.56,
13.19,
12.88,
12.22,
9.69,
10.44,
8.88,
9.19,
10.03,
11.22
]
] |
Summarize the behavior of the data points: <ts><ts/>. | open_ended_question | The data initially shows a pronounced upward spike, after which a downward correction follows, showing stabilization with minor fluctuations. The points indicate a recovery after a decline and tend towards stabilization towards the end. | [
[
-2305.79,
-2310.42,
-2309.27,
-2296.86,
-2287.14,
-2282.93,
-2284.05,
-2283.58
]
] |
Calculate the average of the dataset <ts><ts/>. | open_ended_question | The average is calculated as the sum of all values divided by the number of values: (6.06 + 4.06 + 1.19 + 3.25 + 2.0 + 2.69 + 3.53 + 4.72 + 4.38 + 5.16 + 5.47 + 5.12 + 6.66 + 7.03 + 7.91 + 8.25 + 9.81 + 9.69 + 8.81 + 8.03 + 6.91 + 4.41 + 3.41 + 0.94) / 24 = 5.63 approximately. | [
[
6.06,
4.06,
1.19,
3.25,
2,
2.69,
3.53,
4.72,
4.38,
5.16,
5.47,
5.12,
6.66,
7.03,
7.91,
8.25,
9.81,
9.69,
8.81,
8.03,
6.91,
4.41,
3.41,
0.94
]
] |
Identify any structural breaks or changes in mean in the data series: <ts><ts/>. | open_ended_question | A structural break can be identified roughly between positions 6 and 10 where the series shifts from positive to negative values and shows variability around zero rather than maintaining a particular direction. This indicates a change in underlying behavior or a disruption. | [
[
0.07,
0.05,
-0.02,
-0.03,
0.02,
0.04,
0.07,
0.07,
0.03,
-0.01,
-0.01,
0.01,
0.02,
0.03,
0.02,
0.01,
0,
0,
-0.01,
-0.01,
0,
0,
-0.03,
-0.05
]
] |
Identify any potential anomalies in the data points <ts><ts/>. | open_ended_question | The data point 27.46 stands out as a potential anomaly, as it's significantly higher than the surrounding values, while most of the other values are relatively closer together. | [
[
16.08,
14,
14.02,
15.26,
22.9,
27.27,
27.46,
19.87,
19.1,
18.48,
16.3,
15.64,
19.6,
22.85,
21.44,
18.42,
21.27,
15.3,
15.23,
21.52,
24.16,
15.42,
14.49,
20.96
]
] |
Identify any anomalies in the following data points: <ts><ts/>. | open_ended_question | There appear to be no significant anomalies in this dataset. While the data points show some fluctuations, they remain within typical ranges seen across the data series without any single point being dramatically off-trend. | [
[
-37891,
-37736.54,
-37569.72,
-37673.5,
-37956.23,
-38005.09,
-37747.93,
-37555.73,
-37629.34,
-37908.1,
-37993.04,
-37752.89,
-37543.21,
-37595.46,
-37859.45,
-37987.29,
-37798.57,
-37589.29,
-37602.25,
-37855.59,
-38030.14,
... |
True or False: The data point 28.53 is an anomaly in the series <ts><ts/>. | true/false | F | [
[
27.94,
27.59,
28.16,
28.53,
26.09,
21.72,
18.56,
18.06,
17.56,
15.88
]
] |
What is the median of the data points <ts><ts/>? Options: A] 55.0 B] 60.0 C] 61.0 | multiple_choice | B | [
[
55,
42,
57,
60,
46,
60,
51,
49,
57,
61,
80,
47,
51,
78,
80,
75,
56,
65,
67,
61,
66,
55,
76,
63
]
] |
Investigate the time series data <ts><ts/> to determine if there is any cyclical pattern. What is your conclusion? | open_ended_question | There is no clear cyclical pattern evident in this subset of the time series data. While there are fluctuations, they do not appear to repeat at regular intervals or follow a consistent cycle that would suggest the presence of a cyclical pattern. | [
[
0.48,
0.43,
0.44,
0.58,
0.67,
0.55,
0.42,
0.3,
0.27,
0.23,
0.22,
0.17,
-0.86,
-1.01,
-1.08,
-0.81,
-0.14,
0.16,
-1.02,
-0.02,
-0.07,
-0.18,
-0.11,
0.14
]
] |
Identify any structural breaks in the dataset <ts><ts/>. | open_ended_question | A structural break occurs between 0.74 and 0.73, indicating a plateau after a steady rise. This suggests a change in the pattern, from increase to a stabilization or beginning of a decline. | [
[
0.54,
0.57,
0.6,
0.62,
0.64,
0.66,
0.68,
0.69,
0.7,
0.71,
0.72,
0.73,
0.73,
0.74,
0.74,
0.74,
0.74,
0.73,
0.73,
0.72,
0.71,
0.7,
0.7,
0.7
]
] |
The data points <ts><ts/> display a clearly defined cyclical pattern. True or False? | true/false | F | [
[
68,
27,
0,
3,
8,
2,
2,
2,
40,
29,
12,
7,
5,
10,
4,
49,
0,
70,
37,
14,
31,
19,
10,
4
]
] |
Rank the following data points in descending order: <ts><ts/>. | open_ended_question | [77.25, 51.24, 33.68, 32.44, 28.72, 28.57, 27.99, 27.17, 24.99, 21.47, 20.37, 17.68, 15.92, 15.11, 14.08, 11.41, 10.0, 8.82, 7.51, 7.45, 5.4, 4.4, 3.46]. Each value is compared and arranged from highest to lowest to form the ranking. | [
[
15.11,
20.37,
33.68,
27.17,
32.44,
27.99,
28.57,
24.99,
21.47,
3.46,
5.4,
4.4,
7.51,
8.82,
28.72,
7.45,
10,
24.97,
17.68,
15.92,
51.24,
77.25,
11.41,
14.08
]
] |
The data points <ts><ts/> display a repetitive pattern. True or False? | true/false | F | [
[
9.12,
11.19,
12.47,
13.22,
13.66,
13.59,
11.59,
9.88,
8.78,
8.62,
8.12,
7.44,
8.16,
9.06,
8.84,
8.16,
7.44,
7.34,
6.09,
3.88,
2.19,
1.19,
0.94,
0.06
]
] |
Compare the variance between the first and second half of the series <ts><ts/>. | open_ended_question | The variance in the first half (0.21 to -0.02) is higher due to more spread-out values and more extreme high and low points such as 0.65 and -0.15. The second half values are mostly centered, with the most extreme being -0.55 and 0.3, resulting in lower variance. Calculation confirms that the first half's variance is i... | [
[
0.21,
-0.23,
0.06,
-0.05,
-0.15,
0.28,
0.65,
0.27,
0.19,
0.59,
-0.06,
-0.02,
-0.3,
-0.39,
-0.55,
-0.14,
0.24,
-0.39,
0,
0.3,
-0.18,
-0.22,
-0.28,
-0.07
]
] |
Identify any cyclical patterns in the following data points: <ts><ts/>. | open_ended_question | The data is indicative of a cyclical pattern, with an initial dip reaching a low at -2.72 and rising steadily to 32.0, then plateauing with minor fluctuations around 30. Suggesting a slow build-up and peak followed by stabilization. | [
[
0.75,
-0.16,
0.59,
0.69,
-1.59,
-2.72,
-2.12,
-1.06,
-1.47,
-0.75,
3.75,
10.38,
15.81,
20.09,
25.31,
29.94,
32,
30.81,
30.09,
30.47,
29.97,
26.53,
23.59,
22.59
]
] |
What is the mean of the data points <ts><ts/>? A] 8.37, B] 10.79, C] 12.26 | multiple_choice | C | [
[
3.19,
0.84,
0.88,
-0.84,
-1.16,
-1.41,
-4.03,
-5.5,
-7.28,
-9.19,
-8.72,
-8.34,
-6.34,
-4.91,
-2.59,
-0.53,
0.12,
36.84,
37.34,
38.66,
38.47,
40.12,
42.34,
42.91
]
] |
Identify any structural breaks in the data series <ts><ts/>. | open_ended_question | A structural break can be observed at approximately -97.5. Following a significant and sustained drop in values, the trend reverses and steadily inclines. This point marks a change in the progression and direction of the series. | [
[
-108,
-117.5,
-124.5,
-125.5,
-122,
-115.5,
-108,
-97.5,
-86.5,
-73.5,
-59.5,
-46,
-33.5,
-20.5,
-10,
-1.5,
6.5,
12.5,
16.5,
17.5,
16.5,
17.5,
12.5,
9.5
]
] |
Based on the most recent segment <ts><ts/>, predict the likely next data point and justify your reasoning. | open_ended_question | One might expect the pattern of reduction in negativity to continue, predicting possibly around -30. The upward trend (or less negative) is steep towards the end, suggesting positivity or reduced negativity in subsequent data. | [
[
-66.5,
-65,
-62.5,
-59.5,
-56,
-53,
-49,
-44,
-38
]
] |
Which segment of the following shows the highest increase: <ts><ts/>? Options: A. <ts><ts/> B. <ts><ts/> C. <ts><ts/> | multiple_choice | A | [
[
10.92,
21.08,
9.91,
10.99,
8.44,
12.29,
28.21,
9.67,
31.71,
13.9,
16.41,
13.64,
23.59,
17.79,
25.41,
17.59,
13.62,
10.42,
22.76,
1.06
],
[
8.44,
12.29,
28.21,
9.67
],
[
21.08,
9.91,
10.99,
8.44
... |
Discuss any cyclical patterns observed in the following data: <ts><ts/>. | open_ended_question | The data shows a potential cyclical pattern, where we observe periods of decreasing values followed by an increase, suggesting a cyclical trend. For example, the values decrease from 975.13 to 960.15 and then exhibit an increase from 961.24 to 984.29 over several points, and this pattern of ups and downs might indicate... | [
[
975.13,
971.55,
967.36,
967.77,
965.75,
965.35,
968.47,
967.22,
965.26,
964.79,
960.15,
961.24,
968.24,
970.13,
973.92,
979.42,
982.34,
984.29,
983.38,
981.41,
977.01,
974.21,
975.32,
976.97
]
] |
Which segment of data shows a higher average: A] <ts><ts/>, B] <ts><ts/>? Options: A, B, or both have equal averages. | multiple_choice | B | [
[
12,
7,
13,
22,
16
],
[
14,
18,
6,
9,
38
]
] |
For the given sequence <ts><ts/>, is it true that the sequence shows an overall increasing trend? | true/false | T | [
[
0.43,
0.46,
0.5,
0.54,
0.57,
0.6,
0.62,
0.64,
0.66,
0.68,
0.69,
0.7,
0.71,
0.72,
0.73,
0.73,
0.74,
0.74,
0.74,
0.74,
0.73,
0.73,
0.72,
0.72
]
] |
Calculate the mean of the following time series data <ts><ts/>. | open_ended_question | The mean can be calculated as (30.44 + 29.66 + 29.25 + 28.5 + 28.94 + 31.53 + 33.0 + 35.16) / 8 = 30.81. This is the average value of the given data points. | [
[
30.44,
29.66,
29.25,
28.5,
28.94,
31.53,
33,
35.16
]
] |
The data points <ts><ts/> most likely indicate which trend pattern: A] Increasing trend. B] Decreasing trend. C] No discernible trend. D] Mixed with initial decrease and later increase. | multiple_choice | D | [
[
1,
1,
0,
0,
6,
1,
16,
20,
20,
26,
20,
6,
4,
7,
7,
5,
0,
49,
34,
37,
50,
50,
53,
47
]
] |
Considering the time series <ts><ts/>, the mean is greater than the median. | true/false | T | [
[
9,
7,
12,
32,
14,
11,
16,
10,
23,
12,
19,
19,
14,
19,
16,
21,
9,
12,
13,
20,
12,
15,
11,
6
]
] |
What are the key summary statistics of this series: <ts><ts/>? | open_ended_question | The key statistics are: Minimum = -0.62, Maximum = 0.46, Mean ≈ 0.113. These statistics are calculated directly from the data, indicating an overall positive inclination. | [
[
-0.62,
-0.57,
-0.53,
-0.49,
-0.44,
-0.36,
-0.27,
-0.18,
-0.06,
0.06,
0.15,
0.23,
0.3,
0.36,
0.39,
0.42,
0.44,
0.45,
0.44,
0.44,
0.46,
0.44,
0.42,
0.39
]
] |
What is the dominant pattern in the time series <ts><ts/>? A] Upward trend B] Downward trend C] Random fluctuations | multiple_choice | C | [
[
21.75,
22.41,
20.81,
20.31,
21.34,
21.41,
19.69,
18.66,
20.12,
21.91
]
] |
Analyze the trend in the data series <ts><ts/>. Describe any noticeable trends. | open_ended_question | There is a noticeable uptrend with large spikes followed by drop-offs. Initially, the data is low, followed by a spike at 71.0, then it decreases and remains low, with another spike at 70.0. It ends with rising values towards 44.0. This shows large volatility with intermittent peaks. | [
[
2,
71,
16,
1,
1,
1,
1,
1,
70,
0,
1,
5,
8,
29,
63,
33,
0,
0,
1,
0,
39,
44,
24,
1
]
] |
The series <ts><ts/> shows a decreasing trend. True or False? | true/false | F | [
[
21,
10,
10,
8,
10,
13,
7,
2,
8,
2,
18,
10,
4,
6,
7,
10,
9,
9,
11,
19
]
] |
Which of the following data segments indicates the highest volatility?
A] <ts><ts/>
B] <ts><ts/>
C] <ts><ts/>
D] <ts><ts/> | multiple_choice | C | [
[
36.5,
35,
31.5,
27,
25,
20.5
],
[
-84.5,
-86.5,
-83,
-82,
-76.5,
-73
],
[
-1.5,
-13,
-24,
-36.5,
-47.5,
-58.5
],
[
22.5,
22,
23,
22.5,
21,
21
]
] |
The dataset <ts><ts/> shows high volatility. True or False? | true/false | F | [
[
0.43,
0.42,
0.47,
0.45,
0.44,
0.42,
0.41,
0.39,
0.38,
0.37,
0.36,
0.36,
0.35,
0.35,
0.34,
0.34,
0.34,
0.34,
0.34,
0.34,
0.34,
0.33,
0.33,
0.33
]
] |
What is the minimum value in the sequence <ts><ts/>? Options: a] 0.05, b] 0.06, c] 0.07, d] 0.08. | multiple_choice | A | [
[
0.06,
0.07,
0.06,
0.1,
0.08,
0.13,
0.09,
0.06,
0.06,
0.16,
0.25,
0.26,
0.32,
0.29,
0.28,
0.08,
0.06,
0.08,
0.08,
0.07,
0.06,
0.08,
0.06,
0.05
]
] |
Which of the following options best describes the volatility in the segment <ts><ts/>? A] Low volatility B] Medium volatility C] High volatility | multiple_choice | B | [
[
13.47,
13.47,
15.47,
17.22,
16.88,
15.88,
16.94,
17.97,
17.16,
15.44,
15.66,
17.22,
17.59,
16.84,
17.44,
19.59,
21.19,
21.09,
21.62,
24.53,
27.31,
27.44,
26.62
]
] |
Assess whether the time series points <ts><ts/> show a cyclical pattern. | open_ended_question | There is a weak cyclical pattern observable when the data periodically returns to values around the mid-20s, however, there are also elements of randomness and abrupt deviations, such as drops to 2, that disrupt a well-defined cycle. | [
[
24,
16,
22,
25,
29,
26,
37,
26,
34,
22,
14,
14,
2,
19,
14,
24,
11,
24,
16,
31,
25,
29,
27,
30
]
] |
Analyze the data points <ts><ts/> and describe any repeating patterns you observe. | open_ended_question | There is a cyclical pattern present. The values rise from a lower value, peak around 32.38, then slowly decline to a lower value around 14.12 before rising again to 21.66. This indicates a cycle in the data. | [
[
23.06,
26.41,
29.66,
30.81,
30.22,
31,
32.38,
32.22,
30.16,
28.94,
28.72,
27.53,
24.19,
20.84,
19.97,
18.91,
16.28,
14.12,
14.78,
16.44,
16.88,
16.69,
18.56,
21.66
]
] |
Do the data points <ts><ts/> exhibit a cyclical pattern? | open_ended_question | No, the data points exhibit a consistent downward trend rather than a cyclical pattern. A cyclical pattern would involve repeating increases and decreases, which is not present here. | [
[
0.55,
0.54,
0.53,
0.52,
0.51,
0.5,
0.49,
0.48,
0.48,
0.47,
0.47,
0.46,
0.46,
0.45,
0.45,
0.44,
0.44,
0.43,
0.43,
0.42,
0.42,
0.41,
0.41,
0.41
]
] |
Analyze the data points <ts><ts/> and describe any observable trend. | open_ended_question | The data shows a clear downward trend in the initial segment, reaching a low around -526.86, followed by a recovery trend. This indicates that the data initially decreases sharply and then begins to recover, suggesting an initial steep decline followed by a gradual upward correction. | [
[
-486.82,
-497.87,
-509.35,
-517.54,
-522.65,
-526.86,
-524.82,
-518.7,
-508.88,
-498.03,
-490.81,
-489.51,
-487.4,
-482.83,
-484.66,
-489.46,
-492.61,
-492.05,
-489.53,
-486.77,
-481.95,
-479.99,
-477.04,
-475.53
]
] |
Across the timeline represented, which point appears to be an anomaly: <ts><ts/>? A] -6960.08 B] -7006.92 C] -6985.25 D] -6991.51 | multiple_choice | A | [
[
-6962.27,
-6959.75,
-6960.08,
-6964.1,
-6966.15,
-6969.29,
-6972.43,
-6977.47,
-6989.34,
-6999.88,
-7002.7,
-7005.97,
-7006.92,
-7005.63,
-7006.67,
-7005.23,
-7007.62,
-7004.75,
-6997.47,
-6993.95,
-6993.59,
-6993.49,
-699... |
What is the mean of the time series <ts><ts/>?
A] 18.52
B] 17.89
C] 19.45
D] 16.73 | multiple_choice | C | [
[
24.16,
24.52,
18.54,
14.47,
14.6,
17.44,
21.6,
16.93,
14.79,
12.61,
12.45,
13.2,
18.69,
20.67,
16.08,
14,
14.02,
15.26,
22.9,
27.27,
27.46,
19.87,
19.1,
18.48
]
] |
Examine the data points <ts><ts/> and identify the overall trend. Is it: a] Upward b] Downward c] Constant d] Cyclical | multiple_choice | B | [
[
0.76,
0.74,
0.72,
0.7,
0.68,
0.65,
0.63,
0.6,
0.57,
0.55,
0.53,
0.51,
0.49,
0.48,
0.46,
0.45,
0.43,
0.42,
0.41,
0.41,
0.4,
0.39,
0.39,
0.39
]
] |
Does the data <ts><ts/> show a repeating pattern? | true/false | F | [
[
-10379.5,
-10378.53,
-10368.47,
-10366.74,
-10383.37,
-10377.92,
-10371.59,
-10367.78,
-10363.33,
-10361.55,
-10375.23,
-10382.3,
-10375.72,
-10368.32,
-10365.77,
-10367.16,
-10366.69,
-10365.93,
-10361.46,
-10357.37,
-10364.07,
... |
What is the mean value of the data points <ts><ts/>? | open_ended_question | The mean value is approximately 0.05. To calculate the mean, sum all the data points (1.15) and divide by the number of points (24): 1.15 / 24 ≈ 0.048, rounded to 0.05. | [
[
0.28,
0.3,
0.31,
0.36,
0.33,
0.2,
0.13,
0,
-0.15,
-0.22,
-0.36,
-0.48,
-0.46,
-0.34,
-0.24,
-0.13,
0.02,
0.11,
0.22,
0.32,
0.25,
0.2,
0.19,
0.1
]
] |
Identify any cyclical pattern in the time series data points <ts><ts/>. | open_ended_question | A cyclical pattern is somewhat evident. The points first show moderate declines and increases that suggest a short-term consistent pattern of down and up cycles, apparent through two visible peaks around values -19668.98 and again around -19659.18, suggesting cyclical characteristics. | [
[
-19665.13,
-19666.66,
-19668.98,
-19666.56,
-19663.97,
-19648.53,
-19652.29,
-19653.56,
-19659.18,
-19665.13,
-19666.66,
-19653.05,
-19650.12,
-19648.53,
-19652.29,
-19653.56,
-19659.18
]
] |
Analyze the data points <ts><ts/>. What major trend can be observed? | open_ended_question | The data shows an overall upward trend. Initially, it fluctuates slightly but begins to rise significantly from the 18th point onwards, increasing steeply from 1.22 to 1.97. This indicates a strong upward trend in the latter part of the series. | [
[
1.19,
1.16,
1.19,
1.17,
1.19,
1.19,
1.18,
1.15,
1.14,
1.12,
1.12,
1.07,
1.03,
1.01,
1.01,
1.07,
1.15,
1.22,
1.34,
1.46,
1.6,
1.8,
1.97,
1.91
]
] |
Using the data points <ts><ts/>, summarize their overall behavior. | open_ended_question | The data points predominantly oscillate around the lower end, with one more noticeable peak at 19.0. It presents an overall stable behavior with minor fluctuations and no significant outliers. | [
[
6,
8,
19,
13,
3,
7,
13,
11,
9,
8,
15,
9,
10,
11,
10,
6,
8,
19,
13,
3,
7,
13,
11,
9
]
] |
Given the series <ts><ts/>, identify the main peak. A] 763.81 B] 767.22 C] 770.3 D] 774.49 | multiple_choice | C | [
[
763.81,
766.82,
764.56,
761.27,
762.47,
767.22,
765.04,
757.44,
760.52,
765.82,
761.82,
759.73,
758.52,
760.72,
770.3,
768.96,
764.28,
760.56,
760.92,
765.28,
763.91,
760.06,
763.2,
766.6
]
] |
Identify any cyclical patterns in the series <ts><ts/>. | open_ended_question | The series shows a cyclical pattern where values rise to a peak and then fall to a low repeatedly. Specifically, the data shows peaks around 28.31 and lows around -10.34, indicating a cyclical pattern of peaks and troughs. | [
[
13.75,
12.88,
11,
10.97,
13.53,
15.91,
16.44,
17.62,
20.75,
24.19,
25.12,
25.22,
26.81,
28.31,
26.47,
22.16,
18.72,
15.28,
9.88,
2.78,
-3.09,
-6.28,
-8,
-10.34
]
] |
Identify any anomalies in the time series data <ts><ts/>. | open_ended_question | The data around 0.34, 0.43, 0.52 exhibits anomalies as they present abrupt changes compared to the rest of the data, indicating a possible structural change or outlier. | [
[
0.34,
0.33,
0.33,
0.32,
0.32,
0.31,
0.31,
0.3,
0.3,
0.29,
0.29,
0.3,
0.34,
0.43,
0.52,
0.61,
0.7,
0.73,
0.73,
0.72,
0.71,
0.7,
0.69,
0.67
]
] |
Based on the values <ts><ts/>, determine the possible point of structural break, if any. | open_ended_question | The likely structural break point is between 9.0 and 29.0. The rapid increase to 29.0 signifies a sudden change in data behavior followed by a sharp decline to 3.0, indicating potential structural shifts. | [
[
8,
9,
29,
3,
12,
17,
14,
11,
11,
11,
12,
12,
7,
5,
12,
17,
5,
8,
2,
8,
10,
9,
11,
3
]
] |
Is there a structural break in the time series data: <ts><ts/>? A] Yes, between points 19.0 and 3.0, B] Yes, between 11.0 and 1.0, C] No structural break present. | multiple_choice | A | [
[
49,
40,
22,
19,
3,
4,
21,
49,
11,
1,
7,
13,
16,
18
]
] |
What general trend is observed in the time series points <ts><ts/>? | open_ended_question | The general trend in the data shows progression from negative values towards positive, indicating an overall upward trend. Early values remain around -0.09 but gradually increase, reaching 0.08. It suggests an initial plateau followed by growth. | [
[
-0.09,
-0.09,
-0.09,
-0.09,
-0.09,
-0.06,
-0.03,
0,
0,
0,
0,
0,
-0.01,
0,
0.01,
0.03,
0.04,
0.05,
0.06,
0.07,
0.06,
0.07,
0.06,
0.08
]
] |
Do the time series points <ts><ts/> show any apparent seasonal patterns? A] Yes B] No | multiple_choice | D | [
[
1.15,
1.08,
1.19,
1.13,
1.01,
0.87,
0.85,
0.91,
0.99,
0.97,
0.94,
0.94,
0.97,
1.09,
1.16,
1.05,
0.95,
1.17,
1.37,
1.68,
1.87,
1.95,
1.66,
1.15
]
] |
Discuss any cyclical patterns in the data <ts><ts/>. | open_ended_question | There is an indication of a potential cyclical pattern with an initial steady increase followed by stabilization and slight decrease. However, further data would be required to confirm distinct cycles or recurrent fluctuations within this segment. | [
[
18.34,
20.41,
21.16,
23.12,
23.62,
24.69,
25.69,
27.09,
27.34,
28.12,
29.12,
29.38,
30.06,
28.66,
27.56,
28.16,
27.78,
29.62,
23.72,
20.19,
20.94,
21.34,
18.09,
16.62
]
] |
What is the mean value of the data subset <ts><ts/>? | open_ended_question | The mean of the data is 279.02. This is calculated by summing up all the data points: 6705.5, and dividing by the number of data points, which is 24. Therefore, mean = 6705.5 / 24 ≈ 279.02. | [
[
21,
67,
116,
162.5,
206,
246.5,
282.5,
313.5,
340,
359.5,
371.5,
379.5,
381,
378,
370.5,
358.5,
343.5,
330,
315.5,
299,
282.5,
266,
255,
241.5
]
] |
Interpret the movement of the following data points: <ts><ts/>. | open_ended_question | Initially, there is a decrease from 10.19 to around 2.66, displaying a declining trend. Then, there is an upward trend to 12.88 followed by stability around 12. However, a drastic drop to negative values indicates an anomalous trend towards the end. | [
[
10.19,
7.66,
5.31,
4.16,
2.66,
3.44,
3.75,
5.53,
5.88,
7.72,
9.5,
10.91,
11.72,
12.88,
12.38,
12.16,
11.09,
10.03,
9.94,
-8.78,
-9.34,
-8.47,
-7.34,
-6.22
]
] |
Identify the highest peak in the series <ts><ts/>. Options: A] 1.0, B] 9.0, C] 7.0, D] 6.0 | multiple_choice | B | [
[
2,
1,
4,
7,
4,
3,
1,
1,
2,
9,
4,
5,
2,
9,
2,
3,
3,
3,
2,
1,
6,
5,
3,
9
]
] |
Is there a cyclical pattern observable in the data points: <ts><ts/>? Options: A] Strong cycles, B] Mild cycles, C] Little to no cycles | multiple_choice | B | [
[
-4728.2,
-4728.08,
-4723.13,
-4722.64,
-4728.45,
-4731.59,
-4733.59,
-4732.62,
-4729.28,
-4729.7,
-4732.8,
-4740.89,
-4742.26,
-4737.82,
-4738.27,
-4737.15,
-4740.2,
-4745.76,
-4742.97,
-4742.49,
-4744.02,
-4744.77,
-4743.... |
Identify the maximum and minimum values in this time series: <ts><ts/>. What are they? | open_ended_question | The maximum value is 4.24 and the minimum value is -4.82. This can be determined by scanning the series and identifying the largest and smallest numbers. | [
[
3.89,
4.24,
3.53,
3.25,
3.47,
3.2,
3.16,
2.71,
1.57,
1.48,
-1.59,
-2.26,
-2.24,
-2.87,
-3.65,
-3.75,
-4.82,
-2.35,
-1.07,
-0.46,
-0.15,
0.52,
0.65,
1.24
]
] |
The following data points exhibit seasonality: <ts><ts/> | true/false | F | [
[
-4720.56,
-4721.98,
-4710.33,
-4707.88,
-4705.96,
-4702.59,
-4700.96,
-4696.59,
-4700.26,
-4709.57,
-4708.58,
-4702.3,
-4700.53,
-4700.65,
-4694.59,
-4702.78,
-4712.94,
-4700.54,
-4694.89,
-4696.25,
-4696.02,
-4701.65,
-47... |
End of preview. Expand in Data Studio
PATRA-TRAIN
Training data for PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering (ICML 2026).
- Code: https://github.com/decisionintelligence/PATRA
- Model: DecisionIntelligence/PATRA-7B
- Eval data: DecisionIntelligence/PATRA-EVAL
Splits
| File | # samples | Stage |
|---|---|---|
sft.jsonl |
27,906 | Alignment stage — supervised fine-tuning |
grpo.jsonl |
27,906 | Reasoning-enhanced stage — GRPO |
Fields
sft.jsonl (columns consumed by LLaMA-Factory via train/data/dataset_info.json):
input(str) — prompt with a<ts><ts/>placeholder marking where the series is fed inoutput(str) — target responsetimeseries(list) — the raw series (paired with a scale channel, shape[[[value], [scale]], ...])
grpo.jsonl (columns consumed by src/llamafactory/train/grpo/workflow.py):
input(str) — prompt with<ts><ts/>question_format(str) — one ofmultiple_choice,true/false,open_ended_question(controls the appended format prompt and reward branch)answer(str) — gold answer (letter for MC,T/Ffor TF, reference text for open-ended)timeseries(list[list[float]]) — the numeric series
Usage
from datasets import load_dataset
sft = load_dataset("DecisionIntelligence/PATRA-TRAIN", name="sft", split="train")
grpo = load_dataset("DecisionIntelligence/PATRA-TRAIN", name="grpo", split="train")
For end-to-end training see train/scripts/train_patra_sft.sh and train/scripts/train_patra_grpo.sh.
Citation
@inproceedings{lu2026patrapatternawarealignmentbalanced,
title={PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering},
author={Junkai Lu and Peng Chen and Xingjian Wu and Yang Shu and Chenjuan Guo and Christian S. Jensen and Bin Yang},
booktitle={ICML},
year={2026}
}
- Downloads last month
- 2