{"url":"/dataset/ett","name":"ETT","full_name":"Electricity Transformer Temperature","description_markdown":"The **Electricity Transformer Temperature** (**ETT**) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months.\r\n\r\nSource: [https://arxiv.org/pdf/2012.07436.pdf](https://arxiv.org/pdf/2012.07436.pdf)\r\nImage Source: [https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)","description_withheld":null,"homepage":"https://github.com/zhouhaoyi/ETDataset","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","first_author":"Haoyi Zhou","url":null},"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","datasets_with_task":"/datasets/task/time-series-forecasting"},{"name":"Multivariate Time Series Forecasting","url":"/task/multivariate-time-series-forecasting","datasets_with_task":"/datasets/task/multivariate-time-series-forecasting"},{"name":"Time Series","url":"/task/time-series-1","datasets_with_task":"/datasets/task/time-series-1"},{"name":"GLinear","url":"/task/glinear","datasets_with_task":"/datasets/task/glinear"},{"name":"Univariate Time Series Forecasting","url":"/task/univariate-time-series-forecasting","datasets_with_task":"/datasets/task/univariate-time-series-forecasting"},{"name":"Multivariate Time Series Forecastingm","url":"/task/multivariate-time-series-forecastingm","datasets_with_task":"/datasets/task/multivariate-time-series-forecastingm"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["ETTm2 (96) Multivariate","ETTm2 (96)","ETTm2 (720) Multivariate","ETTm2 (336) Multivariate","ETTm2 (192) Multivariate","ETTm1 (96)","ETTm1 (720) Multivariate","ETTm1 (720)","ETTm1 (336) Multivariate","ETTm1 (336)","ETTm1 (192) Multivariate","ETTh1 (96) Multivariate","ETTh1 (720) Multivariate","ETTh1 (336) Multivariate","ETTh1 (192) Multivariate","ETTh2 (96)","ETTh2 (192)","ETTh1 (96)","ETTh1 (192)","ETTh2 (720)","ETTh2 (168)","ETTh1 (336)","ETTh1 (168)","ETTh2 (48)","ETTh2 (336)","ETTh2 (24)","ETTh1 (720)","ETTh1 (48)","ETTh1 (24)","ETT"],"data_loaders":[{"repo":"https://github.com/zhouhaoyi/ETDataset","url":"https://github.com/zhouhaoyi/ETDataset","frameworks":[]}],"num_papers_in_archive":321,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-forecasting-on-etth1-336-1","task":"Time Series Forecasting","dataset_variant":"ETTh1 (336) Multivariate","rows":72,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"D-PAD","paper":"/paper/d-pad-deep-shallow-multi-frequency-patterns","metrics":{"MAE":"0.406","MSE":"0.374"},"code_links":[{"title":"xybbo5/d-pad","url":"https://github.com/xybbo5/d-pad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-etth1-720-1","task":"Time Series Forecasting","dataset_variant":"ETTh1 (720) Multivariate","rows":22,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"DiPE-Linear","paper":"/paper/disentangled-interpretable-representation-for","metrics":{"MSE":"0.409"},"code_links":[{"title":"wintertee/dipe-linear","url":"https://github.com/wintertee/dipe-linear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-etth1-192-1","task":"Time Series Forecasting","dataset_variant":"ETTh1 (192) Multivariate","rows":17,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"PatchMixer","paper":"/paper/patchmixer-a-patch-mixing-architecture-for","metrics":{"MAE":"0.394","MSE":"0.373"},"code_links":[{"title":"Zeying-Gong/PatchMixer","url":"https://github.com/Zeying-Gong/PatchMixer"},{"title":"hughxx/tsf-new-paper-taste","url":"https://github.com/hughxx/tsf-new-paper-taste"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-etth1-96-1","task":"Time Series Forecasting","dataset_variant":"ETTh1 (96) Multivariate","rows":15,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"SegRNN","paper":"/paper/segrnn-segment-recurrent-neural-network-for","metrics":{"MAE":"0.376","MSE":"0.341"},"code_links":[{"title":"thuml/Time-Series-Library","url":"https://github.com/thuml/Time-Series-Library"},{"title":"WenjieDu/PyPOTS","url":"https://github.com/WenjieDu/PyPOTS"},{"title":"lss-1138/SegRNN","url":"https://github.com/lss-1138/SegRNN"},{"title":"hughxx/tsf-new-paper-taste","url":"https://github.com/hughxx/tsf-new-paper-taste"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm1-192-1","task":"Time Series Forecasting","dataset_variant":"ETTm1 (192) Multivariate","rows":9,"metrics":["MSE","MAE","Accuracy"],"first_row_in_archive_order":{"model":"xPatch","paper":"/paper/xpatch-dual-stream-time-series-forecasting","metrics":{"Accuracy":"0.355","MSE":"0.315"},"code_links":[{"title":"stitsyuk/xpatch","url":"https://github.com/stitsyuk/xpatch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm2-192-1","task":"Time Series Forecasting","dataset_variant":"ETTm2 (192) Multivariate","rows":9,"metrics":["MSE","MAE","MSE "],"first_row_in_archive_order":{"model":"xPatch","paper":"/paper/xpatch-dual-stream-time-series-forecasting","metrics":{"MAE":"0.280","MSE":"0.213"},"code_links":[{"title":"stitsyuk/xpatch","url":"https://github.com/stitsyuk/xpatch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm2-96-1","task":"Time Series Forecasting","dataset_variant":"ETTm2 (96) Multivariate","rows":9,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"xPatch","paper":"/paper/xpatch-dual-stream-time-series-forecasting","metrics":{"MAE":"0.240","MSE":"0.153"},"code_links":[{"title":"stitsyuk/xpatch","url":"https://github.com/stitsyuk/xpatch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm1-336-1","task":"Time Series Forecasting","dataset_variant":"ETTm1 (336) Multivariate","rows":8,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"LTBoost (drop_last=false)","paper":"/paper/ltboost-boosted-hybrids-of-ensemble-linear","metrics":{"MAE":"0.375","MSE":"0.349"},"code_links":[{"title":"hubtru/LTBoost","url":"https://github.com/hubtru/LTBoost"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm1-720-1","task":"Time Series Forecasting","dataset_variant":"ETTm1 (720) Multivariate","rows":8,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"LTBoost (drop_last=false)","paper":"/paper/ltboost-boosted-hybrids-of-ensemble-linear","metrics":{"MAE":"0.405","MSE":"0.403"},"code_links":[{"title":"hubtru/LTBoost","url":"https://github.com/hubtru/LTBoost"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm2-336-1","task":"Time Series Forecasting","dataset_variant":"ETTm2 (336) Multivariate","rows":8,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"LTBoost (drop_last=false)","paper":"/paper/ltboost-boosted-hybrids-of-ensemble-linear","metrics":{"MAE":"0.317","MSE":"0.262"},"code_links":[{"title":"hubtru/LTBoost","url":"https://github.com/hubtru/LTBoost"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-ettm2-720-1","task":"Time Series Forecasting","dataset_variant":"ETTm2 (720) Multivariate","rows":8,"metrics":["MSE","MAE"],"first_row_in_archive_order":{"model":"xPatch","paper":"/paper/xpatch-dual-stream-time-series-forecasting","metrics":{"MAE":"0.363","MSE":"0.338"},"code_links":[{"title":"stitsyuk/xpatch","url":"https://github.com/stitsyuk/xpatch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-etth1","task":"Multivariate Time Series Forecasting","dataset_variant":"ETTh1 (96) Multivariate","rows":3,"metrics":["MAE","MSE"],"first_row_in_archive_order":{"model":"TSMixer","paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","metrics":{"MAE":"0.398","MSE":"0.368"},"code_links":[{"title":"ibm/tsfm","url":"https://github.com/ibm/tsfm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-etth1-1","task":"Multivariate Time Series Forecasting","dataset_variant":"ETTh1 (192) Multivariate","rows":3,"metrics":["MAE","MSE"],"first_row_in_archive_order":{"model":"TSMixer","paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","metrics":{"MAE":"0.418","MSE":"0.399"},"code_links":[{"title":"ibm/tsfm","url":"https://github.com/ibm/tsfm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-etth1-2","task":"Multivariate Time Series Forecasting","dataset_variant":"ETTh1 (336) Multivariate","rows":2,"metrics":["MSE"],"first_row_in_archive_order":{"model":"MMFNet","paper":"/paper/mmfnet-multi-scale-frequency-masking-neural","metrics":{"MSE":"0.409"},"code_links":[{"title":"vewoxic/fits","url":"https://github.com/vewoxic/fits"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/glinear-on-etth1-24","task":"GLinear","dataset_variant":"ETTh1 (24)","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"GLinear","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE":"0.3142"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/glinear-on-etth1-336","task":"GLinear","dataset_variant":"ETTh1 (336)","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"GLinear","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE":"0.4915"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/glinear-on-etth1-48","task":"GLinear","dataset_variant":"ETTh1 (48)","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"MSE","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE":"0.3537"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/glinear-on-etth1-720-multivariate","task":"GLinear","dataset_variant":"ETTh1 (720) Multivariate","rows":1,"metrics":["MSE "],"first_row_in_archive_order":{"model":"GLinear","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE ":"0.5923"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-etth1-3","task":"Multivariate Time Series Forecasting","dataset_variant":"ETTh1 (720) Multivariate","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"TSMixer","paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","metrics":{"MSE":"0.444"},"code_links":[{"title":"ibm/tsfm","url":"https://github.com/ibm/tsfm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-forecasting-on-etth1-48-3","task":"Time Series Forecasting","dataset_variant":"ETTh1 (48)","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"Time-LLM","paper":"/paper/time-llm-time-series-forecasting-by","metrics":{"MSE":"0.408"},"code_links":[{"title":"kimmeen/time-llm","url":"https://github.com/kimmeen/time-llm"},{"title":"kwuking/TimeMixer","url":"https://github.com/kwuking/TimeMixer"},{"title":"WenjieDu/PyPOTS","url":"https://github.com/WenjieDu/PyPOTS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/bridging-simplicity-and-sophistication-using-1","title":"Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction","date":"2025-01-02","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/xpatch-dual-stream-time-series-forecasting","title":"xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition","date":"2024-12-23","rows_on_this_dataset":11,"code_links":1,"syntology":null},{"paper":"/paper/disentangled-interpretable-representation-for","title":"Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting","date":"2024-11-26","rows_on_this_dataset":11,"code_links":1,"syntology":null},{"paper":"/paper/ltboost-boosted-hybrids-of-ensemble-linear","title":"LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting","date":"2024-10-21","rows_on_this_dataset":11,"code_links":1,"syntology":null},{"paper":"/paper/mmfnet-multi-scale-frequency-masking-neural","title":"MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series Forecasting","date":"2024-10-02","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/prformer-pyramidal-recurrent-transformer-for","title":"PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting","date":"2024-08-20","rows_on_this_dataset":13,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fredformer-frequency-debiased-transformer-for","title":"Fredformer: Frequency Debiased Transformer for Time Series Forecasting","date":"2024-06-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deformtime-capturing-variable-dependencies","title":"DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-multi-scale-decomposition-framework","title":"Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting","date":"2024-06-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/timecma-towards-llm-empowered-time-series","title":"TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment","date":"2024-06-03","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/forecastgrapher-redefining-multivariate-time","title":"ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks","date":"2024-05-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rose-register-assisted-general-time-series","title":"ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning","date":"2024-05-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/leveraging-2d-information-for-long-term-time","title":"Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers","date":"2024-05-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/attention-as-an-rnn","title":"Attention as an RNN","date":"2024-05-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vcformer-variable-correlation-transformer","title":"VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting","date":"2024-05-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/time-evidence-fusion-network-multi-source","title":"Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting","date":"2024-05-10","rows_on_this_dataset":11,"code_links":2,"syntology":null},{"paper":"/paper/boosting-mlps-with-a-coarsening-strategy-for","title":"Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting","date":"2024-05-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sparsetsf-modeling-long-term-time-series","title":"SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters","date":"2024-05-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mamba-360-survey-of-state-space-models-as","title":"Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges","date":"2024-04-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bi-mamba4ts-bidirectional-mamba-for-time","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","date":"2024-04-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/softs-efficient-multivariate-time-series","title":"SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion","date":"2024-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/atfnet-adaptive-time-frequency-ensembled","title":"ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting","date":"2024-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/d-pad-deep-shallow-multi-frequency-patterns","title":"D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting","date":"2024-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/an-analysis-of-linear-time-series-forecasting","title":"An Analysis of Linear Time Series Forecasting Models","date":"2024-03-21","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/is-mamba-effective-for-time-series","title":"Is Mamba Effective for Time Series Forecasting?","date":"2024-03-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/timemachine-a-time-series-is-worth-4-mambas","title":"TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting","date":"2024-03-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/taming-pre-trained-llms-for-generalised-time","title":"CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning","date":"2024-03-12","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cats-enhancing-multivariate-time-series","title":"CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables","date":"2024-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/convtimenet-a-deep-hierarchical-fully","title":"ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis","date":"2024-03-03","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/units-building-a-unified-time-series-model","title":"UniTS: A Unified Multi-Task Time Series Model","date":"2024-02-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":28,"samples_ran":19,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/generative-pretrained-hierarchical","title":"Generative Pretrained Hierarchical Transformer for Time Series Forecasting","date":"2024-02-26","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/random-projection-layers-for-multidimensional","title":"RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data","date":"2024-02-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unlocking-the-potential-of-transformers-in","title":"SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention","date":"2024-02-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/only-the-curve-shape-matters-training","title":"Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction","date":"2024-02-12","rows_on_this_dataset":6,"code_links":0,"syntology":null},{"paper":"/paper/unified-training-of-universal-time-series","title":"Unified Training of Universal Time Series Forecasting Transformers","date":"2024-02-04","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/pathformer-multi-scale-transformers-with","title":"Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting","date":"2024-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/minusformer-improving-time-series-forecasting","title":"Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals","date":"2024-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/autotimes-autoregressive-time-series","title":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models","date":"2024-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-channel-dependence-for","title":"Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators","date":"2024-01-31","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/himtm-hierarchical-multi-scale-masked-time","title":"HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting","date":"2024-01-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/ttms-fast-multi-level-tiny-time-mixers-for","title":"Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series","date":"2024-01-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mixture-of-linear-experts-for-long-term-time","title":"Mixture-of-Linear-Experts for Long-term Time Series Forecasting","date":"2023-12-11","rows_on_this_dataset":15,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/winnet-time-series-forecasting-with-a-window","title":"WinNet: Make Only One Convolutional Layer Effective for Time Series Forecasting","date":"2023-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/basisformer-attention-based-time-series-1","title":"BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis","date":"2023-10-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":6,"samples_unverified":6,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unitime-a-language-empowered-unified-model","title":"UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting","date":"2023-10-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-decoder-only-foundation-model-for-time","title":"A decoder-only foundation model for time-series forecasting","date":"2023-10-14","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/itransformer-inverted-transformers-are","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","date":"2023-10-10","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":8,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tempo-prompt-based-generative-pre-trained","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","date":"2023-10-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":6,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/time-llm-time-series-forecasting-by","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","date":"2023-10-03","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/patchmixer-a-patch-mixing-architecture-for","title":"PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting","date":"2023-10-01","rows_on_this_dataset":4,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/segrnn-segment-recurrent-neural-network-for","title":"SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting","date":"2023-08-22","rows_on_this_dataset":4,"code_links":4,"syntology":null},{"paper":"/paper/fits-modeling-time-series-with-10k-parameters","title":"FITS: Modeling Time Series with $10k$ Parameters","date":"2023-07-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","date":"2023-06-14","rows_on_this_dataset":15,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-structured-components-towards","title":"Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting","date":"2023-05-22","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisiting-long-term-time-series-forecasting","title":"Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping","date":"2023-05-18","rows_on_this_dataset":11,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/long-term-forecasting-with-tide-time-series","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","date":"2023-04-17","rows_on_this_dataset":4,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tsmixer-an-all-mlp-architecture-for-time","title":"TSMixer: An All-MLP Architecture for Time Series Forecasting","date":"2023-03-10","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/a-time-series-is-worth-64-words-long-term","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","date":"2022-11-27","rows_on_this_dataset":4,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":30,"samples_ran":7,"samples_unverified":23,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/are-transformers-effective-for-time-series","title":"Are Transformers Effective for Time Series Forecasting?","date":"2022-05-26","rows_on_this_dataset":6,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":13,"samples_unverified":6,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/film-frequency-improved-legendre-memory-model","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","date":"2022-05-18","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":17,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/taming-pre-trained-language-models-with-n","title":"Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation","date":"2021-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/long-term-series-forecasting-with-query","title":"Long-term series forecasting with Query Selector -- efficient model of sparse attention","date":"2021-07-19","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/autoformer-decomposition-transformers-with","title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","date":"2021-06-24","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":13,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/time-series-is-a-special-sequence-forecasting","title":"SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction","date":"2021-06-17","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","date":"2020-12-14","rows_on_this_dataset":2,"code_links":14,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":76,"samples_ran":62,"samples_unverified":14,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":32,"samples_harvested":348,"samples_ran":220,"samples_unverified":128,"pointer_only_for_licence":82,"papers_with_no_sample_that_ran":5,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}