Datasets:
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Error code: DatasetGenerationError Exception: CastError Message: Couldn't cast item_id: string start: timestamp[s] freq: string target: list<item: float> child 0, item: float past_feat_dynamic_real: fixed_size_list<item: list<item: float>>[7] child 0, item: list<item: float> child 0, item: float -- schema metadata -- huggingface: '{"info": {"features": {"item_id": {"dtype": "string", "_typ' + 347 to {'item_id': Value(dtype='string', id=None), 'start': Value(dtype='timestamp[s]', id=None), 'freq': Value(dtype='string', id=None), 'target': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)} because column names don't match Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single for _, table in generator: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 76, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 59, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast item_id: string start: timestamp[s] freq: string target: list<item: float> child 0, item: float past_feat_dynamic_real: fixed_size_list<item: list<item: float>>[7] child 0, item: list<item: float> child 0, item: float -- schema metadata -- huggingface: '{"info": {"features": {"item_id": {"dtype": "string", "_typ' + 347 to {'item_id': Value(dtype='string', id=None), 'start': Value(dtype='timestamp[s]', id=None), 'freq': Value(dtype='string', id=None), 'target': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)} because column names don't match 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 1417, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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item_id
string | start
timestamp[s] | freq
string | target
sequence |
---|---|---|---|
0 | 2015-01-01T00:00:00 | 5T | [61.93913650512695,59.23252487182617,61.99180221557617,62.480655670166016,62.490482330322266,62.5417(...TRUNCATED) |
1 | 2015-01-01T00:00:00 | 5T | [64.2808837890625,65.08245086669922,65.30912017822266,65.191650390625,65.28766632080078,68.001235961(...TRUNCATED) |
2 | 2015-01-01T00:00:00 | 5T | [62.077396392822266,64.80834197998047,64.80391693115234,67.20659637451172,67.32328796386719,66.41279(...TRUNCATED) |
3 | 2015-01-01T00:00:00 | 5T | [60.78642272949219,65.85395050048828,64.26608276367188,63.988426208496094,64.70740509033203,62.72486(...TRUNCATED) |
4 | 2015-01-01T00:00:00 | 5T | [63.12067413330078,59.20623016357422,62.239200592041016,65.80850982666016,65.70866394042969,63.67469(...TRUNCATED) |
5 | 2015-01-01T00:00:00 | 5T | [64.44831848144531,62.4967155456543,63.816612243652344,64.75755310058594,65.35836791992188,63.123970(...TRUNCATED) |
6 | 2015-01-01T00:00:00 | 5T | [63.41112518310547,65.99217987060547,60.19683074951172,62.01144790649414,65.09144592285156,62.115444(...TRUNCATED) |
7 | 2015-01-01T00:00:00 | 5T | [64.7394790649414,64.71804809570312,65.44779205322266,66.33447265625,63.09504699707031,68.0616760253(...TRUNCATED) |
8 | 2015-01-01T00:00:00 | 5T | [63.009918212890625,61.24407196044922,63.79776382446289,61.702735900878906,62.18679428100586,61.0574(...TRUNCATED) |
9 | 2015-01-01T00:00:00 | 5T | [65.26490020751953,65.60872650146484,66.01715850830078,65.73542785644531,65.09737396240234,65.096916(...TRUNCATED) |
GIFT-Eval
We present GIFT-Eval, a benchmark designed to advance zero-shot time series forecasting by facilitating evaluation across diverse datasets. GIFT-Eval includes 23 datasets covering 144,000 time series and 177 million data points, with data spanning seven domains, 10 frequencies, and a range of forecast lengths. This benchmark aims to set a new standard, guiding future innovations in time series foundation models.
To facilitate the effective pretraining and evaluation of foundation models, we also provide a non-leaking pretraining dataset --> GiftEvalPretrain.
Submitting your results
If you want to submit your own results to our leaderborad please follow the instructions detailed in our github repository
Citation
If you find this benchmark useful, please consider citing:
@article{aksu2024giftevalbenchmarkgeneraltime,
title={GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation},
author={Taha Aksu and Gerald Woo and Juncheng Liu and Xu Liu and Chenghao Liu and Silvio Savarese and Caiming Xiong and Doyen Sahoo},
journal = {arxiv preprint arxiv:2410.10393},
year={2024},
}
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