input stringlengths 159 1.58k | output stringlengths 98 13.4k | timeseries listlengths 1 23 | eval_type stringclasses 51
values | eval_metadata stringlengths 48 534 |
|---|---|---|---|---|
You are a time series analysis expert. This is a metric called User Activity collected from Social Media with length of 81: <ts>. If a 'significant upward shift' is when the difference between second-half and first-half means is more than 15% of the total range, does User Activity qualify? | <think>
metric: User Activity
stats: max=79.95, min=13.43, first_half_mean=48.10, second_half_mean=28.76
===
range = 79.95 - 13.43 = 66.52
shift = 28.76 - 48.10 = -19.34
|shift| = 19.34
threshold = 15% × 66.52 = 9.98
19.34 > 9.98 -> met
answer: yes
</think>
Yes. User Activity's mean shifts significantly between halves... | [
[
50.319246,
50.313482,
50.29619,
79.94737,
50.227021,
50.175144,
50.11174,
50.036807,
49.950346,
49.852357,
49.74284,
49.621795,
49.489221,
49.34512,
49.18949,
49.022332,
48.843646,
48.653432,
48.45169,
48.23842,
48.013621,
47.... | segment_judgment | {"judgment_type": "half_mean_shift", "verdict": "yes", "length": 81, "tsevol": false} |
In an Advertising system, there are 10 metrics:
Engagement Rates on Ad Content is of length 256: <ts>;
Campaign Reach is of length 256: <ts>;
Attribution Metrics is of length 256: <ts>;
Video Completion Rates is of length 256: <ts>;
Social Media Ad Performance is of length 256: <ts>;
Ad Frequency is of length 256... | <think>
metric: Engagement Rates on Ad Content
trend_segments=[(increase, 0, 255)]
---
metric: Video Completion Rates
trend_segments=[(decrease, 0, 255)]
===
threshold=2.
comparison with Engagement Rates on Ad Content
vs Video Completion Rates: segments count: 1 vs 1. seg 0: type increase ≠ decrease. FAIL.
answer: diff... | [
[
7.553012,
7.569893,
7.597657,
7.568316,
7.611129,
7.77914,
7.931604,
7.859522,
7.691188,
7.692333,
7.69418,
7.709237,
7.754803,
7.726179,
7.764055,
7.757782,
7.797529,
7.786007,
7.833648,
7.84219,
7.856823,
7.867752,
7.863... | correlation | {"metric_a": "Engagement Rates on Ad Content", "metric_b": "Video Completion Rates", "mode": "shape", "length": 256, "verdict": "different", "tsevol": false} |
In an Internet of Things (IoT) system, there are 9 metrics:
Smart Lighting Usage is of length 256: <ts>;
Smart Home Metrics is of length 256: <ts>;
Wearable Device Data is of length 256: <ts>;
Health Monitoring Devices is of length 256: <ts>;
Device Uptime/Downtime is of length 256: <ts>;
Water Quality Sensors is... | <think>
metric: Smart Lighting Usage
start=-0.51, end=-0.25, amp=0.26, overall trend=increase
trend_segments=[(increase, 0, 155), (decrease, 155, 255)]
</think>
From the perspective of the slope, the overall trend is increasing. The value of time series starts from around -0.51 and ends at around -0.25, with an overal... | [
[
-554.552834,
-535.881737,
-538.6504,
-523.150733,
-514.374829,
-502.32157,
-490.046021,
-476.841906,
-444.559462,
-447.141986,
-443.55328,
-409.256695,
-401.053788,
-392.482284,
-373.96781,
-354.624706,
-354.828838,
-337.143491,
-330.4947... | description | {"perspectives": ["trend"], "metric_index": 0, "length": 256, "tsevol": false} |
In a Manufacturing system, there are 13 metrics:
Maintenance Schedules is of length 256: <ts>;
Downtime Logs is of length 256: <ts>;
Equipment Utilization is of length 256: <ts>;
Safety Incident Rates is of length 256: <ts>;
Inventory Levels is of length 256: <ts>;
Machine Performance is of length 256: <ts>;
Qua... | <think>
Let me look at the timeseries carefully.
Looking at the Downtime Logs timeseries (256 data points).
The minimum value of -1663187.95 occurs at index 159.
answer: 159
</think>
For Downtime Logs, the lowest value is at position 159. | [
[
-0.354261,
-0.378228,
-0.386434,
-0.406975,
-0.378872,
-0.403445,
-0.329792,
-0.36726,
-0.29401,
-0.360344,
-0.396578,
-0.38435,
-0.386309,
-0.39152,
-0.416849,
-0.357787,
-0.359897,
-0.369881,
-0.32867,
-0.363386,
-0.378996,
... | atomic_min_position | {"length": 256, "verdict": "159", "direction": "min", "tsevol": false} |
You are a time series analysis expert. This is a metric called Undo Tablespace Usage collected from Oracle Database with length of 256: <ts>. Check whether Undo Tablespace Usage has a noticeable event around point 103 within the tolerance of 20 timesteps. | <think>
metric: Undo Tablespace Usage
local=[sudden decrease, start@(9, 389.57); sudden decrease, start@(76, 347.07); downward spike, start@(214, 288.97)]
===
threshold=20. anchor=103.
Undo Tablespace Usage: |9-103|=94 > 20; |76-103|=27 > 20; |214-103|=111 > 20. FAIL.
answer: no
</think>
I did not find any local event... | [
[
393.637441,
393.185033,
392.732643,
392.280287,
391.827982,
391.375745,
390.923593,
390.471544,
390.019613,
389.567819,
386.523321,
383.478994,
380.434853,
377.390917,
374.347202,
371.303725,
368.260504,
367.627328,
368.492562,
370.19... | yes_no | {"target_point": 103, "metric": "Undo Tablespace Usage", "threshold": 20, "variant": "generic", "verdict": "no", "length": 256, "tsevol": false} |
In a Finance system, there are 7 metrics:
Volume Traded is of length 256: <ts>;
Stock Prices is of length 256: <ts>;
Dividend Yields is of length 256: <ts>;
Exchange Rates is of length 256: <ts>;
Market Indices is of length 256: <ts>;
Cryptocurrency Prices is of length 256: <ts>;
Inflation Rates is of length 256... | <think>
metric: Cryptocurrency Prices
trend_segments=[(increase, 0, 66), (decrease, 66, 255)]
===
durations:
(increase, 0, 66): duration = 66 - 0 = 66
(decrease, 66, 255): duration = 255 - 66 = 189
aggregate:
increase: 66
decrease: 189
decrease: 189 > 66 -> winner = decrease
answer: decrease
</think>
Cryptocur... | [
[
139.856541,
120.121509,
107.644389,
88.236778,
63.828286,
24.718832,
24.373145,
-12.485533,
-15.280939,
-26.304056,
-38.468855,
-74.710935,
-94.750586,
-118.165415,
-128.040678,
-129.070112,
-137.630422,
-180.311948,
-208.382899,
-236... | duration_proportion | {"sub_type": "duration_comparison", "verdict": "decrease", "length": 256, "tsevol": false} |
You are a time series analysis expert. This is a metric called Engagement Rates on Ad Content collected from Advertising with length of 256: <ts>. Where does the last local event appear in Engagement Rates on Ad Content? | <think>
metric: Engagement Rates on Ad Content
local=[sudden increase, pos=30; sudden decrease, pos=207]
===
sorted positions: 30, 207
latest = 207
answer: 207
</think>
The last local event in Engagement Rates on Ad Content occurs at position 207. | [
[
14.548736,
20.198397,
25.616435,
29.271793,
30.708193,
30.632328,
30.592903,
32.360959,
37.219863,
45.4038,
55.876729,
66.53443,
74.775586,
78.267556,
75.667336,
67.06917,
54.034208,
39.187897,
25.506018,
15.507503,
10.600412,
... | temporal_position | {"sub_type": "last_event", "verdict": "207", "length": 256, "tsevol": false} |
In an Agriculture system, there are 5 metrics:
Organic vs. Conventional Farming Metrics is of length 256: <ts>;
Greenhouse Gas Emissions from Farming is of length 256: <ts>;
Livestock Counts is of length 256: <ts>;
Water Usage Efficiency is of length 256: <ts>;
Feed Conversion Ratios is of length 256: <ts>. In Org... | <think>
metric: Organic vs. Conventional Farming Metrics
trend_segments=[(decrease, 0, 76), (increase, 76, 185), (decrease, 185, 255)]
===
intersecting [91, 242] with Organic vs. Conventional Farming Metrics trend segments:
- (increase, 76, 185) ∩ [91, 242] = [91, 185] -> duration = 185 - 91 = 94
- (decrease, 185, ... | [
[
94.265225,
90.324971,
92.742834,
75.458174,
81.782144,
69.383363,
72.336015,
66.628428,
65.453624,
55.699366,
53.079427,
47.571201,
51.64542,
41.359895,
38.733815,
31.472398,
26.50093,
22.971703,
19.393955,
19.435467,
17.776222,
... | segment_trend_dominance | {"verdict": "increase", "length": 256, "tsevol": false} |
In a Manufacturing system, there are 13 metrics:
Machine Performance is of length 256: <ts>;
Production Line Efficiency is of length 256: <ts>;
Lead Times is of length 256: <ts>;
Inventory Turnover is of length 256: <ts>;
Manufacturing Costs is of length 256: <ts>;
Production Scheduling Metrics is of length 256: ... | <think>
metric: Energy Usage in Production
start=0.41, end=0.69, amp=0.28, overall trend=increase
trend_segments=[(increase, 0, 67), (decrease, 67, 194), (increase, 194, 255)]
</think>
From the perspective of the slope, the overall trend is increasing. The value of time series starts from around 0.41 and ends at aroun... | [
[
333.749906,
341.216045,
347.346193,
355.407747,
360.817207,
364.108558,
374.462195,
380.379256,
387.165879,
395.115385,
395.413345,
404.774045,
406.947132,
417.065661,
420.588532,
428.010036,
430.749504,
437.990707,
441.496668,
444.76... | description | {"perspectives": ["trend"], "metric_index": 11, "length": 256, "tsevol": false} |
You are a time series analysis expert. This is a metric called Feature Toggle Metrics collected from Application Performance with length of 256: <ts>. Describe the segments of the trend in Feature Toggle Metrics over time. | <think>
Let me look at how the timeseries evolves.
From 0 to 255 the data is decrease (255 data points)
In total there are 1 trend phases.
answer: 1
</think>
The Feature Toggle Metrics timeseries consists of 1 distinct trend segments: a single decrease phase from index 0 to 255. | [
[
13.860538,
13.449242,
13.037961,
12.626711,
12.215508,
11.804367,
11.393303,
10.982332,
10.57147,
10.160731,
9.750132,
9.339687,
8.929413,
8.519324,
8.109436,
7.699765,
7.290326,
6.881134,
6.472205,
6.063554,
5.655196,
5.24714... | atomic_trend_enumeration | {"length": 256, "verdict": "1", "n_segments": 1, "tsevol": true, "evol_strategy": "paraphrase"} |
In a Weather Forecasting system, there are 4 metrics:
Storm Tracking is of length 256: <ts>;
Atmospheric Pressure is of length 256: <ts>;
Wind Speed is of length 256: <ts>;
Weather Alert Frequencies is of length 256: <ts>. Between decrease and increase trend segments in Weather Alert Frequencies, which accumulates ... | <think>
metric: Weather Alert Frequencies
trend_segments=[(increase, 0, 140), (decrease, 140, 255)]
===
durations:
(increase, 0, 140): duration = 140 - 0 = 140
(decrease, 140, 255): duration = 255 - 140 = 115
aggregate:
increase: 140
decrease: 115
increase: 140 > 115 -> winner = increase
answer: increase
</thin... | [
[
-19249.147395,
-24959.692243,
-27724.541679,
-31258.179903,
-35646.157106,
-39198.386384,
-45836.316585,
-49206.595314,
-53554.660374,
-57187.801203,
-61822.577036,
-66001.062101,
-69437.790659,
-72655.220504,
-77560.9914,
-80236.832436,
-83540.0... | duration_proportion | {"sub_type": "duration_comparison", "verdict": "increase", "length": 256, "tsevol": false} |
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