Twice Upon a Time: Complementary Representations for Time-Series Reasoning
Paper • 2610.22327 • Published • 4
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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
question string | options list | answer string | question_type string | ts list | tid int64 | difficulty string | format_hint string | relevant_concepts list | question_hint string | attributes dict | caption string | category string | subcategory string | id int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
There is a time series of length 256. | [] | From the perspective of the slope, the overall trend is decreasing. The value of time series starts from around 95.22 and ends at around 55.26, with an overall amplitude of -39.96. No seasonality. Almost no noise. In terms of local characteristics, starting from point 78, the time series value falls from around 91.48 t... | caption | [
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94... | 200 | medium | [
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"segments": [
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]
},
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"frequency": {
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},
"... | Overall, the series declines steadily; with a downward convex between 78 and 116, a downward spike between 146 and 148. | Captioning | Aspect Caption | 1 | ||
There is a time series of length 256. | [] | No seasonality. Almost no noise. In terms of the trend and changes of this time series: At the beginning, from point 0 to 78, the time series decreases with values from 95.22 to 91.48; from point 78 to point 116, starting from point 78, the time series value falls from around 91.48 to around 42.40, forms a downward con... | caption | [
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},
"... | Overall, the series declines steadily; with a downward convex between 78 and 116, a downward spike between 146 and 148. | Captioning | Chronological Caption | 2 | ||
There is a time series of length 256. | [] | A falling, smooth series with 2 short-lived changes. | caption | [
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94... | 202 | medium | [
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] | {
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]
]
},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
"period": 0
},
"... | Overall, the series declines steadily; with a downward convex between 78 and 116, a downward spike between 146 and 148. | Captioning | Summary Caption | 3 | ||
There is a time series of length 256. | [] | Overall, the series declines steadily; with a downward convex between 78 and 116, a downward spike between 146 and 148. | caption | [
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94... | 203 | medium | [
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] | {
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]
},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
"period": 0
},
"... | Overall, the series declines steadily; with a downward convex between 78 and 116, a downward spike between 146 and 148. | Captioning | Paraphrase Caption | 4 | ||
There is a time series of length 256. | [] | {"seq_len":256,"trend":{"type":"decrease","start":95.22,"amplitude":-39.96,"segments":[["decrease",0,255]]},"seasonal":{"type":"no periodic fluctuation"},"frequency":{"type":"no periodicity","period":0.0},"noise":{"type":"almost no noise","std":0.01},"local":[{"type":"downward convex","position_start":78,"position_end"... | caption | [
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94... | 204 | medium | [
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]
]
},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
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},
"... | Overall, the series declines steadily; with a downward convex between 78 and 116, a downward spike between 146 and 148. | Captioning | JSON Caption | 5 | ||
There is a time series of length 256. | [] | From the perspective of the slope, the overall trend contains multiple different segments: From point 0 to point 62, there is a decreasing trend. From point 62 to point 193, there is an increasing trend with some variation in slope. From point 193 to point 255, there is a decreasing trend. However, local phase changes ... | caption | [
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},... | A piecewise-trending series with 2 short-lived changes. | Captioning | Aspect Caption | 6 | ||
There is a time series of length 256. | [] | No seasonality. Almost no noise. | caption | [
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},... | A piecewise-trending series with 2 short-lived changes. | Captioning | Chronological Caption | 7 | ||
There is a time series of length 256. | [] | A piecewise-trending series with 2 short-lived changes. | caption | [
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"Local:sudden increase"
] | {
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],
[
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255
]
]
},... | A piecewise-trending series with 2 short-lived changes. | Captioning | Summary Caption | 8 | ||
There is a time series of length 256. | [] | Overall, with an increase after downward spike between 152 and 159, a sudden increase between 248 and 249; the series follows a piecewise trend. | caption | [
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-1211074.7833... | 203 | medium | [
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"Seasonal:no periodic fluctuation",
"Noise:almost no noise",
"Local:increase after downward spike",
"Local:sudden increase"
] | {
"seq_len": 256,
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[
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62,
193
],
[
"decrease",
193,
255
]
]
},... | A piecewise-trending series with 2 short-lived changes. | Captioning | Paraphrase Caption | 9 | ||
There is a time series of length 256. | [] | {"seq_len":256,"trend":{"type":"multiple","start":-1031588.81,"amplitude":2878446.6,"segments":[["decrease",0,62],["increase",62,193],["decrease",193,255]]},"seasonal":{"type":"no periodic fluctuation"},"frequency":{"type":"no periodicity","period":0.0},"noise":{"type":"almost no noise","std":491.91},"local":[{"type":"... | caption | [
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-1211074.7833... | 204 | medium | [
"Trend:multiple",
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] | {
"seq_len": 256,
"trend": {
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62,
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],
[
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193,
255
]
]
},... | A piecewise-trending series with 2 short-lived changes. | Captioning | JSON Caption | 10 | ||
There is a time series of length 256. | [] | From the perspective of the slope, the overall trend is steady. The value of time series starts from around -57.76 and ends at around -57.76, with an overall amplitude of 0.00. No seasonality. The overall noise standard deviation is around 9.70, indicating a large noisy curve. No local characteristics are found. | caption | [
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-70.895... | 200 | medium | [
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] | {
"seq_len": 256,
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]
},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
"period": 0
},
... | A level, noisy series. | Captioning | Aspect Caption | 11 | ||
There is a time series of length 256. | [] | No seasonality. The overall noise standard deviation is around 9.70, indicating a large noisy curve. In terms of the trend and changes of this time series: Overall, from point 0 to 255, the time series stays steady with values from -46.23 to -54.26. | caption | [
-46.2255,
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-70.895... | 201 | medium | [
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] | {
"seq_len": 256,
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},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
"period": 0
},
... | A level, noisy series. | Captioning | Chronological Caption | 12 | ||
There is a time series of length 256. | [] | A level, noisy series. | caption | [
-46.2255,
-51.8317,
-72.3882,
-53.5128,
-60.7183,
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-52.4299,
-62.5099,
-51.2135,
-70.895... | 202 | medium | [
"Trend:keep steady",
"Seasonal:no periodic fluctuation",
"Noise:noisy"
] | {
"seq_len": 256,
"trend": {
"type": "keep steady",
"start": -57.76,
"amplitude": 0,
"segments": [
[
"keep steady",
0,
255
]
]
},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
"period": 0
},
... | A level, noisy series. | Captioning | Summary Caption | 13 | ||
There is a time series of length 256. | [] | The series shows no real trend; no seasonality is present; it carries noticeable noise (noise std ~ 9.7). | caption | [
-46.2255,
-51.8317,
-72.3882,
-53.5128,
-60.7183,
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-70.895... | 203 | medium | [
"Trend:keep steady",
"Seasonal:no periodic fluctuation",
"Noise:noisy"
] | {
"seq_len": 256,
"trend": {
"type": "keep steady",
"start": -57.76,
"amplitude": 0,
"segments": [
[
"keep steady",
0,
255
]
]
},
"seasonal": {
"type": "no periodic fluctuation"
},
"frequency": {
"type": "no periodicity",
"period": 0
},
... | A level, noisy series. | Captioning | Paraphrase Caption | 14 | ||
There is a time series of length 256. | [] | {"seq_len":256,"trend":{"type":"keep steady","start":-57.76,"amplitude":0.0,"segments":[["keep steady",0,255]]},"seasonal":{"type":"no periodic fluctuation"},"frequency":{"type":"no periodicity","period":0.0},"noise":{"type":"noisy","std":9.7},"local":[]} | caption | [
-46.2255,
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... | A level, noisy series. | Captioning | JSON Caption | 15 | ||
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There is a time series of length 256. | [] | From the perspective of the slope, the overall trend is increasing. However, local phase changes were observed, including: decrease after downward spike. The value of time series starts from around -84.09 and ends at around -165.27, with an overall amplitude of -81.18. No seasonality. Almost no noise. In terms of local... | caption | [
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... | In summary, the series rises overall; locally there is a decrease after downward spike between 63 and 72, a slow decline followed by rapid rise between 157 and 176. | Captioning | Aspect Caption | 21 | ||
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There is a time series of length 256. | [] | From the perspective of the slope, the overall trend is increasing. However, local phase changes were observed, including: sudden decrease. The value of time series starts from around -3934.55 and ends at around -3817.91, with an overall amplitude of 116.64. No seasonality. The overall noise standard deviation is aroun... | caption | [
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There is a time series of length 256. | [] | A increasing, noisy series with a single sudden decrease between 103 and 109. | caption | [
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There is a time series of length 256. | [] | In summary, the series climbs steadily; it carries noticeable noise (noise std ~ 1009.59); locally there is a sudden decrease between 103 and 109. | caption | [
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"... | {"seq_len":256,"trend":{"type":"keep steady","start":0.34,"amplitude":0.0,"segments":[["keep steady",0,255]]},"seasonal":{"type":"no periodic fluctuation"},"frequency":{"type":"no periodicity","period":0.0},"noise":{"type":"almost no noise","std":0.0},"local":[{"type":"upward spike","position_start":86,"position_end":8... | Captioning | JSON Caption | 45 | ||
There is a time series of length 256. | [] | From the perspective of the slope, the overall trend is increasing. The value of time series starts from around -6.62 and ends at around -6.31, with an overall amplitude of 0.31. No seasonality. The overall noise standard deviation is around 0.04, indicating a large noisy curve. No local characteristics are found. | caption | [
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There is a time series of length 256. | [] | No seasonality. The overall noise standard deviation is around 0.04, indicating a large noisy curve. In terms of the trend and changes of this time series: Overall, from point 0 to 255, the time series increases with values from -6.63 to -6.23. | caption | [
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There is a time series of length 256. | [] | A growing, noisy series. | caption | [
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There is a time series of length 256. | [] | Overall, the signal is noisy (noise std ~ 0.04); the series rises overall. | caption | [
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"no... | No seasonality. The overall noise standard deviation is around 0.04, indicating a large noisy curve. In terms of the trend and changes of this time series: Overall, from point 0 to 255, the time series increases with values from -6.63 to -6.23. | Captioning | Paraphrase Caption | 49 | ||
There is a time series of length 256. | [] | {"seq_len":256,"trend":{"type":"increase","start":-6.62,"amplitude":0.31,"segments":[["increase",0,255]]},"seasonal":{"type":"no periodic fluctuation"},"frequency":{"type":"no periodicity","period":0.0},"noise":{"type":"noisy","std":0.04},"local":[]} | caption | [
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"no... | No seasonality. The overall noise standard deviation is around 0.04, indicating a large noisy curve. In terms of the trend and changes of this time series: Overall, from point 0 to 255, the time series increases with values from -6.63 to -6.23. | Captioning | JSON Caption | 50 | ||
There is a time series of length 256. | [] | From the perspective of the slope, the overall trend is increasing. However, local phase changes were observed, including: sudden decrease. The value of time series starts from around -4.42 and ends at around -4.77, with an overall amplitude of -0.35. No seasonality. Almost no noise. In terms of local characteristics, ... | caption | [
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"seasonal": {
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"frequency": {
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},
"n... | Locally there is a sudden decrease between 188 and 191, an upward spike between 246 and 248; the series trends upward; it is aperiodic. | Captioning | Aspect Caption | 51 | ||
There is a time series of length 256. | [] | No seasonality. Almost no noise. In terms of the trend and changes of this time series: At the beginning, from point 0 to 188, the time series increases with values from -4.42 to -3.29; from point 188 to point 191, a sudden decrease with an amplitude of 2.52 occurred between point 188 and point 191, with the time serie... | caption | [
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"Noise:almost no noise",
"Local:sudden decrease",
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"frequency": {
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"n... | Locally there is a sudden decrease between 188 and 191, an upward spike between 246 and 248; the series trends upward; it is aperiodic. | Captioning | Chronological Caption | 52 |
End of preview.
DualTime-data
The in-house training corpora of Twice Upon a Time: Complementary Representations for Time-Series Reasoning, named as in the paper's training-data table. Raw JSON / JSONL with the series as number arrays; the charts and delay-embedding images are rendered at training time by the code repository.
| Stage | File | Rows | Supervision |
|---|---|---|---|
| A | stageA/chatts_style_captions.jsonl |
32,000 | caption |
| A | stageA/structural_captions.json |
7,701 | caption |
| B | stageB/tsexam_perception.json |
20,640 | multiple choice |
| B | stageB/numerical_corpus.json |
11,000 | scalar answer |
| B | stageB/reasoning_corpus.json |
100,000 | multiple choice, with rationales |
The other training sources come from their own repositories: CaTS-Bench
(mhfisher/CaTSBench, Stage A) and the ChatTS ift / sft sets
(ChatTSRepo/ChatTS-Training-Dataset, Stage B).
Usage
hf download SequentialLearning/DualTime-data --repo-type dataset --local-dir data
data/ is where the code repository's training scripts read the corpora
(STAGE=A bash scripts/train.sh, STAGE=B bash scripts/train.sh).
Licences
structural_captions, tsexam_perception and numerical_corpus are
generated with a fork of the TimeSeriesExam generator (MIT);
chatts_style_captions is built on series from the ChatTS time-series
generator (see that dataset's card); reasoning_corpus is this project's own
synthetic data.
Citation
@misc{zisling2026twicetime,
title={Twice Upon a Time: Complementary Representations for Time-Series Reasoning},
author={Hedi Zisling and Liran Nochumsohn and Ilan Naiman and Mihir Parmar and Omri Azencot},
year={2026},
eprint={2610.22327},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2610.22327},
}
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