Datasets:
ett

- {"h1": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. 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The train/val/test is 12/4/4 months.\n", "citation": "@inproceedings{haoyietal-informer-2021,\n author = {Haoyi Zhou and\n Shanghang Zhang and\n Jieqi Peng and\n Shuai Zhang and\n Jianxin Li and\n Hui Xiong and\n Wancai Zhang},\n title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},\n booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},\n volume = {35},\n number = {12},\n pages = {11106--11115},\n publisher = {{AAAI} Press},\n year = {2021},\n}\n", "homepage": "https://github.com/zhouhaoyi/ETDataset", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"start": {"dtype": "timestamp[s]", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_static_cat": {"feature": {"dtype": "uint64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_dynamic_real": {"feature": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "item_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ett", "config_name": "h2", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 241978, "num_examples": 1, "dataset_name": "ett"}, "test": {"name": "test", "num_bytes": 77508960, "num_examples": 240, "dataset_name": "ett"}, "validation": {"name": "validation", "num_bytes": 33916080, "num_examples": 120, "dataset_name": "ett"}}, "download_checksums": {"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv": {"num_bytes": 2417960, "checksum": "a3dc2c597b9218c7ce1cd55eb77b283fd459a1d09d753063f944967dd6b9218b"}}, "download_size": 2417960, "post_processing_size": null, "dataset_size": 111667018, "size_in_bytes": 114084978}, "m1": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. The train/val/test is 12/4/4 months.\n", "citation": "@inproceedings{haoyietal-informer-2021,\n author = {Haoyi Zhou and\n Shanghang Zhang and\n Jieqi Peng and\n Shuai Zhang and\n Jianxin Li and\n Hui Xiong and\n Wancai Zhang},\n title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},\n booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},\n volume = {35},\n number = {12},\n pages = {11106--11115},\n publisher = {{AAAI} Press},\n year = {2021},\n}\n", "homepage": "https://github.com/zhouhaoyi/ETDataset", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"start": {"dtype": "timestamp[s]", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_static_cat": {"feature": {"dtype": "uint64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_dynamic_real": {"feature": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "item_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ett", "config_name": "m1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 967738, "num_examples": 1, "dataset_name": "ett"}, "test": {"name": "test", "num_bytes": 1239008640, "num_examples": 960, "dataset_name": "ett"}, "validation": {"name": "validation", "num_bytes": 542089920, "num_examples": 480, "dataset_name": "ett"}}, "download_checksums": {"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTm1.csv": {"num_bytes": 10360719, "checksum": "6ce1759b1a18e3328421d5d75fadcb316c449fcd7cec32820c8dafda71986c9e"}}, "download_size": 10360719, "post_processing_size": null, "dataset_size": 1782066298, "size_in_bytes": 1792427017}, "m2": {"description": "The data of Electricity Transformers from two separated counties\nin China collected for two years at hourly and 15-min frequencies.\nEach data point consists of the target value \"oil temperature\" and\n6 power load features. The train/val/test is 12/4/4 months.\n", "citation": "@inproceedings{haoyietal-informer-2021,\n author = {Haoyi Zhou and\n Shanghang Zhang and\n Jieqi Peng and\n Shuai Zhang and\n Jianxin Li and\n Hui Xiong and\n Wancai Zhang},\n title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},\n booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},\n volume = {35},\n number = {12},\n pages = {11106--11115},\n publisher = {{AAAI} Press},\n year = {2021},\n}\n", "homepage": "https://github.com/zhouhaoyi/ETDataset", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"start": {"dtype": "timestamp[s]", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_static_cat": {"feature": {"dtype": "uint64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "feat_dynamic_real": {"feature": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "item_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ett", "config_name": "m2", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 967738, "num_examples": 1, "dataset_name": "ett"}, "test": {"name": "test", "num_bytes": 1239008640, "num_examples": 960, "dataset_name": "ett"}, "validation": {"name": "validation", "num_bytes": 542089920, "num_examples": 480, "dataset_name": "ett"}}, "download_checksums": {"https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTm2.csv": {"num_bytes": 9677236, "checksum": "db973ca252c6410a30d0469b13d696cf919648d0f3fd588c60f03fdbdbadd1fd"}}, "download_size": 9677236, "post_processing_size": null, "dataset_size": 1782066298, "size_in_bytes": 1791743534}}