# BasicTS
**Repository Path**: mdongfeng/BasicTS
## Basic Information
- **Project Name**: BasicTS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 0
- **Created**: 2025-06-28
- **Last Updated**: 2025-11-14
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
一个公平、可扩展的时间序列预测基准库和工具包
[**English**](./README.md) **|**
[**简体中文**](./README_CN.md)
---
[](https://github.com/cnstark/easytorch)
[](https://github.com/zezhishao/BasicTS/blob/master/LICENSE)
[](https://pytorch.org/)
[](https://pytorch.org/)
[](https://github.com/zezhishao/BasicTS/blob/master/.github/workflows/pylint.yml)
🎉 [**快速上手**](./tutorial/getting_started_cn.md) **|**
💡 [**总体设计**](./tutorial/overall_design_cn.md)
📦 [**数据集 (Dataset)**](./tutorial/dataset_design_cn.md) **|**
🛠️ [**数据缩放 (Scaler)**](./tutorial/scaler_design_cn.md) **|**
🧠 [**模型约定 (Model)**](./tutorial/model_design_cn.md) **|**
📉 [**评估指标 (Metrics)**](./tutorial/metrics_design_cn.md)
🏃♂️ [**执行器 (Runner)**](./tutorial/runner_design_cn.md) **|**
📜 [**配置文件 (Config)**](./tutorial/config_design_cn.md) **|**
📜 [**基线模型 (Baselines)**](./baselines/)
$\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries) 是一个面向时间序列预测的基准库和工具箱，现已支持时空预测、长序列预测等多种任务与数据集，涵盖统计模型、机器学习模型、深度学习模型等多类算法，为开发和评估时间序列预测模型提供了理想的工具。你可以在[快速上手](./tutorial/getting_started_cn.md)找到详细的教程。
🎉 **更新（2025年6月）：** 添加了6个长序列预测基线：CARD、TimeXer、Bi-Mamba、ModernTCN、S-D-Mamba、S4。
🎉 **更新（2025年5月）：** BasicTS 现已支持使用 [BLAST](https://arxiv.org/abs/2505.17871) 语料库训练通用预测模型（例如 **TimeMoE** 和 **ChronosBolt**）。BLAST 能够实现 **更快的收敛速度**、**显著降低计算成本**，并且即使在资源有限的情况下也能获得卓越性能。[查看](./tutorial/training_with_BLAST_cn.md)。
如果你觉得这个项目对你有帮助，别忘了给个⭐Star支持一下，非常感谢！
> [!IMPORTANT]
> 如果本项目对您有用，请考虑引用下面的论文:
>
> ```LaTeX
> @article{shao2024exploring,
> title={Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis},
> author={Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Sun, Tao and Jin, Guangyin and Cao, Xin and others},
> journal={IEEE Transactions on Knowledge and Data Engineering},
> year={2024},
> volume={37},
> number={1},
> pages={291-305},
> publisher={IEEE}
> }
> ```
>
> 🔥🔥🔥 ***该论文已被IEEE TKDE录用！你可以在这里[查看论文](https://arxiv.org/abs/2310.06119)。*** 🔥🔥🔥
## ✨ 主要功能亮点
BasicTS 一方面通过 **统一且标准化的流程**，为热门的深度学习模型提供了 **公平且全面** 的复现与对比平台。
另一方面，BasicTS 提供了用户 **友好且易于扩展** 的接口，帮助快速设计和评估新模型。用户只需定义模型结构，便可轻松完成基本操作。
### 公平的性能评估：
通过统一且全面的流程，用户能够公平且充分地对比不同模型在任意数据集上的性能表现。
### 使用 BasicTS 进行开发你可以：
最简代码实现
用户只需实现关键部分如模型架构、数据预处理和后处理，即可构建自己的深度学习项目。
基于配置文件控制一切
用户可以通过配置文件掌控流程中的所有细节，包括数据加载器的超参数、优化策略以及其他技巧（如课程学习）。
支持所有设备
BasicTS 支持 CPU、GPU 以及分布式 GPU 训练（单节点多 GPU 和多节点），依托 EasyTorch 作为后端。用户只需通过设置参数即可使用这些功能，无需修改代码。
保存训练日志
BasicTS 提供 `logging` 日志系统和 `Tensorboard` 支持，并统一封装接口，用户可以通过简便的接口调用来保存自定义的训练日志。
## 🚀 安装和快速上手
详细的安装步骤请参考 [快速上手](./tutorial/getting_started_cn.md) 教程。
## 📦 支持的模型
BasicTS 实现了丰富的基线模型，包括经典模型、时空预测模型、长序列预测模型、通用预测模型等。
这些模型的代码实现可在 [baselines](./baselines) 目录中找到。
下表中的代码链接（💻Code） 指向了相关论文的官方实现，感谢各位作者对代码的开源贡献！
通用预测模型
| 📊Baseline | 📝Title | 📄Paper | 💻Code | 🏛Venue | 🎯Task |
| :--------- | :------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------- | :----- |
| TimeMoE | Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts | [Link](https://openreview.net/forum?id=e1wDDFmlVu) | [Link](https://github.com/Time-MoE/Time-MoE) | ICLR'25 | UFM |
| ChronosBolt | Chronos: Learning the Language of Time Series | [Link](https://arxiv.org/abs/2403.07815) | [Link](https://github.com/amazon-science/chronos-forecasting) | TMLR'24 | UFM |
MOIRAI (inference) | Unified Training of Universal Time Series Forecasting Transformers | [Link](https://arxiv.org/abs/2402.02592) | [Link](https://github.com/SalesforceAIResearch/uni2ts) | ICML'24 | UFM |
时空预测
| 📊Baseline | 📝Title | 📄Paper | 💻Code | 🏛Venue | 🎯Task |
| :--------- | :------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------- | :----- |
| STPGNN | Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting | [Link](https://ojs.aaai.org/index.php/AAAI/article/view/28707) | [Link](https://github.com/Kongwy5689/STPGNN?tab=readme-ov-file) | AAAI'24 | STF |
| BigST | Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks | [Link](https://dl.acm.org/doi/10.14778/3641204.3641217) | [Link](https://github.com/usail-hkust/BigST?tab=readme-ov-file) | VLDB'24 | STF |
| STDMAE | Spatio-Temporal-Decoupled Masked Pre-training for Traffic Forecasting | [Link](https://arxiv.org/abs/2312.00516) | [Link](https://github.com/Jimmy-7664/STD-MAE) | IJCAI'24 | STF |
| STWave | When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks | [Link](https://ieeexplore.ieee.org/document/10184591) | [Link](https://github.com/LMissher/STWave) | ICDE'23 | STF |
| STAEformer | Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting | [Link](https://arxiv.org/abs/2308.10425) | [Link](https://github.com/XDZhelheim/STAEformer) | CIKM'23 | STF |
| MegaCRN | Spatio-Temporal Meta-Graph Learning for Traffic Forecasting | [Link](https://aps.arxiv.org/abs/2212.05989) | [Link](https://github.com/deepkashiwa20/MegaCRN) | AAAI'23 | STF |
| DGCRN | Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution | [Link](https://arxiv.org/abs/2104.14917) | [Link](https://github.com/tsinghua-fib-lab/Traffic-Benchmark) | ACM TKDD'23 | STF |
| STID | Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting | [Link](https://arxiv.org/abs/2208.05233) | [Link](https://github.com/zezhishao/STID) | CIKM'22 | STF |
| STEP | Pretraining Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting | [Link](https://arxiv.org/abs/2206.09113) | [Link](https://github.com/GestaltCogTeam/STEP?tab=readme-ov-file) | SIGKDD'22 | STF |
| D2STGNN | Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting | [Link](https://arxiv.org/abs/2206.09112) | [Link](https://github.com/zezhishao/D2STGNN) | VLDB'22 | STF |
| STNorm | Spatial and Temporal Normalization for Multi-variate Time Series Forecasting | [Link](https://dl.acm.org/doi/10.1145/3447548.3467330) | [Link](https://github.com/JLDeng/ST-Norm/blob/master/models/Wavenet.py) | SIGKDD'21 | STF |
| STGODE | Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting | [Link](https://arxiv.org/abs/2106.12931) | [Link](https://github.com/square-coder/STGODE) | SIGKDD'21 | STF |
| GTS | Discrete Graph Structure Learning for Forecasting Multiple Time Series | [Link](https://arxiv.org/abs/2101.06861) | [Link](https://github.com/chaoshangcs/GTS) | ICLR'21 | STF |
| StemGNN | Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting | [Link](https://arxiv.org/abs/2103.07719) | [Link](https://github.com/microsoft/StemGNN) | NeurIPS'20 | STF |
| MTGNN | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks | [Link](https://arxiv.org/abs/2005.11650) | [Link](https://github.com/nnzhan/MTGNN) | SIGKDD'20 | STF |
| AGCRN | Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting | [Link](https://arxiv.org/abs/2007.02842) | [Link](https://github.com/LeiBAI/AGCRN) | NeurIPS'20 | STF |
| GWNet | Graph WaveNet for Deep Spatial-Temporal Graph Modeling | [Link](https://arxiv.org/abs/1906.00121) | [Link](https://github.com/nnzhan/Graph-WaveNet/blob/master/model.py) | IJCAI'19 | STF |
| STGCN | Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting | [Link](https://arxiv.org/abs/1709.04875) | [Link](https://github.com/VeritasYin/STGCN_IJCAI-18) | IJCAI'18 | STF |
| DCRNN | Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting | [Link](https://arxiv.org/abs/1707.01926) | [Link1](https://github.com/chnsh/DCRNN_PyTorch/blob/pytorch_scratch/model/pytorch/dcrnn_cell.py), [Link2](https://github.com/chnsh/DCRNN_PyTorch/blob/pytorch_scratch/model/pytorch/dcrnn_model.py) | ICLR'18 | STF |
Long-Term Time Series Forecasting
| 📊Baseline | 📝Title | 📄Paper | 💻Code | 🏛Venue | 🎯Task |
| :------------ | :------------------------------------------------------------------------------------------------------- | :----------------------------------------------------- | :---------------------------------------------------------------------------- | :--------- | :----- |
| S-D-Mamba | Is Mamba Effective for Time Series Forecasting? | [Link](https://arxiv.org/abs/2403.11144v3) | [Link](https://github.com/wzhwzhwzh0921/S-D-Mamba) | NeuroComputing'24 | LTSF |
| Bi-Mamba | Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting | [Link](https://arxiv.org/abs/2404.15772) | [Link](https://github.com/Leopold2333/Bi-Mamba4TS) | arXiv'24 | LTSF |
| ModernTCN | ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis | [Link](https://openreview.net/forum?id=vpJMJerXHU) | [Link](https://github.com/luodhhh/ModernTCN) | ICLR'24 | LTSF |
| TimeXer | TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables | [Link](https://arxiv.org/abs/2402.19072) | [Link](https://github.com/thuml/TimeXer) | NeurIPS'24 | LTSF |
| CARD | CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting | [Link](https://arxiv.org/abs/2305.12095) | [Link](https://github.com/wxie9/CARD) | ICLR'24 | LTSF |
| SOFTS | SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion | [Link](https://arxiv.org/pdf/2404.14197) | [Link](https://github.com/Secilia-Cxy/SOFTS) | NeurIPS'24 | LTSF |
| CATS | Are Self-Attentions Effective for Time Series Forecasting? | [Link](https://arxiv.org/pdf/2405.16877) | [Link](https://github.com/dongbeank/CATS) | NeurIPS'24 | LTSF |
| Sumba | Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics | [Link](https://xiucheng.org/assets/pdfs/nips24-sumba.pdf) | [Link](https://github.com/chenxiaodanhit/Sumba/) | NeurIPS'24 | LTSF |
| GLAFF | Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective | [Link](https://arxiv.org/pdf/2409.18696) | [Link](https://github.com/ForestsKing/GLAFF) | NeurIPS'24 | LTSF |
| CycleNet | CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns Forecasting | [Link](https://arxiv.org/pdf/2409.18479) | [Link](https://github.com/ACAT-SCUT/CycleNet) | NeurIPS'24 | LTSF |
| Fredformer | Fredformer: Frequency Debiased Transformer for Time Series Forecasting | [Link](https://arxiv.org/pdf/2406.09009) | [Link](https://github.com/chenzRG/Fredformer) | KDD'24 | LTSF |
| UMixer | An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting | [Link](https://arxiv.org/abs/2401.02236) | [Link](https://github.com/XiangMa-Shaun/U-Mixer) | AAAI'24 | LTSF |
| TimeMixer | Decomposable Multiscale Mixing for Time Series Forecasting | [Link](https://arxiv.org/html/2405.14616v1) | [Link](https://github.com/kwuking/TimeMixer) | ICLR'24 | LTSF |
| Time-LLM | Time-LLM: Time Series Forecasting by Reprogramming Large Language Models | [Link](https://arxiv.org/abs/2310.01728) | [Link](https://github.com/KimMeen/Time-LLM) | ICLR'24 | LTSF |
| SparseTSF | Modeling LTSF with 1k Parameters | [Link](https://arxiv.org/abs/2405.00946) | [Link](https://github.com/lss-1138/SparseTSF) | ICML'24 | LTSF |
| iTrainsformer | Inverted Transformers Are Effective for Time Series Forecasting | [Link](https://arxiv.org/abs/2310.06625) | [Link](https://github.com/thuml/iTransformer) | ICLR'24 | LTSF |
| Koopa | Learning Non-stationary Time Series Dynamics with Koopman Predictors | [Link](https://arxiv.org/abs/2305.18803) | [Link](https://github.com/thuml/Koopa) | NeurIPS'24 | LTSF |
| CrossGNN | CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement | [Link](https://openreview.net/pdf?id=xOzlW2vUYc) | [Link](https://github.com/hqh0728/CrossGNN) | NeurIPS'23 | LTSF |
| NLinear | Are Transformers Effective for Time Series Forecasting? | [Link](https://arxiv.org/abs/2205.13504) | [Link](https://github.com/cure-lab/DLinear) | AAAI'23 | LTSF |
| Crossformer | Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting | [Link](https://openreview.net/forum?id=vSVLM2j9eie) | [Link](https://github.com/Thinklab-SJTU/Crossformer) | ICLR'23 | LTSF |
| DLinear | Are Transformers Effective for Time Series Forecasting? | [Link](https://arxiv.org/abs/2205.13504) | [Link](https://github.com/cure-lab/DLinear) | AAAI'23 | LTSF |
| DSformer | A Double Sampling Transformer for Multivariate Time Series Long-term Prediction | [Link](https://arxiv.org/abs/2308.03274) | [Link](https://github.com/ChengqingYu/DSformer) | CIKM'23 | LTSF |
| SegRNN | Segment Recurrent Neural Network for Long-Term Time Series Forecasting | [Link](https://arxiv.org/abs/2308.11200) | [Link](https://github.com/lss-1138/SegRNN) | arXiv | LTSF |
| MTS-Mixers | Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing | [Link](https://arxiv.org/abs/2302.04501) | [Link](https://github.com/plumprc/MTS-Mixers) | arXiv | LTSF |
| LightTS | Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP | [Link](https://arxiv.org/abs/2207.01186) | [Link](https://github.com/thuml/Time-Series-Library/blob/main/models/LightTS.py) | arXiv | LTSF |
| ETSformer | Exponential Smoothing Transformers for Time-series Forecasting | [Link](https://arxiv.org/abs/2202.01381) | [Link](https://github.com/salesforce/ETSformer) | arXiv | LTSF |
| NHiTS | Neural Hierarchical Interpolation for Time Series Forecasting | [Link](https://arxiv.org/abs/2201.12886) | [Link](https://github.com/cchallu/n-hits) | AAAI'23 | LTSF |
| PatchTST | A Time Series is Worth 64 Words: Long-term Forecasting with Transformers | [Link](https://arxiv.org/abs/2211.14730) | [Link](https://github.com/yuqinie98/PatchTST) | ICLR'23 | LTSF |
| TiDE | Long-term Forecasting with TiDE: Time-series Dense Encoder | [Link](https://arxiv.org/abs/2304.08424) | [Link](https://github.com/lich99/TiDE) | TMLR'23 | LTSF |
| S4 | Efficiently Modeling Long Sequences with Structured State Spaces | [Link](https://openreview.net/pdf?id=uYLFoz1vlAC) | [Link](https://github.com/state-spaces/s4) | ICLR'22 | LTSF |
| TimesNet | Temporal 2D-Variation Modeling for General Time Series Analysis | [Link](https://openreview.net/pdf?id=ju_Uqw384Oq) | [Link](https://github.com/thuml/TimesNet) | ICLR'23 | LTSF |
| Triformer | Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting | [Link](https://arxiv.org/abs/2204.13767) | [Link](https://github.com/razvanc92/triformer) | IJCAI'22 | LTSF |
| NSformer | Exploring the Stationarity in Time Series Forecasting | [Link](https://arxiv.org/abs/2205.14415) | [Link](https://github.com/thuml/Nonstationary_Transformers) | NeurIPS'22 | LTSF |
| FiLM | Frequency improved Legendre Memory Model for LTSF | [Link](https://arxiv.org/abs/2205.08897) | [Link](https://github.com/tianzhou2011/FiLM) | NeurIPS'22 | LTSF |
| FEDformer | Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting | [Link](https://arxiv.org/abs/2201.12740v3) | [Link](https://github.com/MAZiqing/FEDformer) | ICML'22 | LTSF |
| Pyraformer | Low complexity pyramidal Attention For Long-range Time Series Modeling and Forecasting | [Link](https://openreview.net/forum?id=0EXmFzUn5I) | [Link](https://github.com/ant-research/Pyraformer) | ICLR'22 | LTSF |
| HI | Historical Inertia: A Powerful Baseline for Long Sequence Time-series Forecasting | [Link](https://arxiv.org/abs/2103.16349) | None | CIKM'21 | LTSF |
| Autoformer | Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting | [Link](https://arxiv.org/abs/2106.13008) | [Link](https://github.com/thuml/Autoformer) | NeurIPS'21 | LTSF |
| Informer | Beyond Efficient Transformer for Long Sequence Time-Series Forecasting | [Link](https://arxiv.org/abs/2012.07436) | [Link](https://github.com/zhouhaoyi/Informer2020) | AAAI'21 | LTSF |
其他方法
| 📊Baseline | 📝Title | 📄Paper | 💻Code | 🏛Venue | 🎯Task |
| :--------- | :------------------------------------------------------------------------ | :--------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------ | :------------------------------------ |
| LightGBM | LightGBM: A Highly Efficient Gradient Boosting Decision Tree | [Link](https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf) | [Link](https://github.com/microsoft/LightGBM) | NeurIPS'17 | Machine Learning |
| NBeats | Neural basis expansion analysis for interpretable time series forecasting | [Link](https://arxiv.org/abs/1905.10437) | [Link1](https://github.com/ServiceNow/N-BEATS), [Link2](https://github.com/philipperemy/n-beats) | ICLR'19 | Deep Time Series Forecasting |
| DeepAR | Probabilistic Forecasting with Autoregressive Recurrent Networks | [Link](https://arxiv.org/abs/1704.04110) | [Link1](https://github.com/jingw2/demand_forecast), [Link2](https://github.com/husnejahan/DeepAR-pytorch), [Link3](https://github.com/arrigonialberto86/deepar) | Int. J. Forecast'20 | Probabilistic Time Series Forecasting |
| WaveNet | WaveNet: A Generative Model for Raw Audio. | [Link](https://arxiv.org/abs/1609.03499) | [Link 1](https://github.com/JLDeng/ST-Norm/blob/master/models/Wavenet.py), [Link 2](https://github.com/huyouare/WaveNet-Theano) | arXiv | Audio |
## 📦 支持的数据集
BasicTS 支持多种类型的数据集，涵盖时空预测、长序列预测及大规模数据集。
时空预测
| 🏷️Name | 🌐Domain | 📏Length | 📊Time Series Count | 🔄Graph | ⏱️Freq. (m) | 🎯Task |
| :------- | :------------ | -------: | ------------------: | :------ | ------------: | :----- |
| METR-LA | Traffic Speed | 34272 | 207 | True | 5 | STF |
| PEMS-BAY | Traffic Speed | 52116 | 325 | True | 5 | STF |
| PEMS03 | Traffic Flow | 26208 | 358 | True | 5 | STF |
| PEMS04 | Traffic Flow | 16992 | 307 | True | 5 | STF |
| PEMS07 | Traffic Flow | 28224 | 883 | True | 5 | STF |
| PEMS08 | Traffic Flow | 17856 | 170 | True | 5 | STF |
长序列预测
| 🏷️Name | 🌐Domain | 📏Length | 📊Time Series Count | 🔄Graph | ⏱️Freq. (m) | 🎯Task |
| :---------------- | :---------------------------------- | -------: | ------------------: | :------ | ------------: | :----- |
| BeijingAirQuality | Beijing Air Quality | 36000 | 7 | False | 60 | LTSF |
| ETTh1 | Electricity Transformer Temperature | 14400 | 7 | False | 60 | LTSF |
| ETTh2 | Electricity Transformer Temperature | 14400 | 7 | False | 60 | LTSF |
| ETTm1 | Electricity Transformer Temperature | 57600 | 7 | False | 15 | LTSF |
| ETTm2 | Electricity Transformer Temperature | 57600 | 7 | False | 15 | LTSF |
| Electricity | Electricity Consumption | 26304 | 321 | False | 60 | LTSF |
| ExchangeRate | Exchange Rate | 7588 | 8 | False | 1440 | LTSF |
| Illness | Ilness Data | 966 | 7 | False | 10080 | LTSF |
| Traffic | Road Occupancy Rates | 17544 | 862 | False | 60 | LTSF |
| Weather | Weather | 52696 | 21 | False | 10 | LTSF |
大规模数据集
| 🏷️Name | 🌐Domain | 📏Length | 📊Time Series Count | 🔄Graph | ⏱️Freq. (m) | 🎯Task |
| :------- | :----------- | -------: | ------------------: | :------ | ------------: | :---------- |
| CA | Traffic Flow | 35040 | 8600 | True | 15 | Large Scale |
| GBA | Traffic Flow | 35040 | 2352 | True | 15 | Large Scale |
| GLA | Traffic Flow | 35040 | 3834 | True | 15 | Large Scale |
| SD | Traffic Flow | 35040 | 716 | True | 15 | Large Scale |
## 📉 主要结果
请参阅论文 *[多变量时间序列预测进展探索：全面基准评测和异质性分析](https://arxiv.org/pdf/2310.06119.pdf)*。
## ✨ 贡献者
感谢这些优秀的贡献者们 ([表情符号指南](https://allcontributors.org/docs/en/emoji-key))：
此项目遵循 [all-contributors](https://github.com/all-contributors/all-contributors) 规范。欢迎任何形式的贡献！
## 🔗 致谢
BasicTS 是基于 [EasyTorch](https://github.com/cnstark/easytorch) 开发的，这是一个易于使用且功能强大的开源神经网络训练框架。
## 📧 联系我们
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