Model overview
The `PatchTSMixer` is a lightweight and fast multivariate time series forecasting model with state-of-the-art performance on benchmark datasets. In this context, we offer a pre-trained `PatchTSMixer` model encompassing all seven channels of the `ETTh1` dataset. This particular pre-trained model produces a Mean Squared Error (MSE) of 0.37 on the `test` split of the `ETTh1` dataset when forecasting 96 hours into the future with a historical data window of 512 hours. The PatchTSMixer model was proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam. PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. In this HuggingFace implementation, we provide PatchTSMixer’s capabilities to effortlessly facilitate lightweight mixing across patches, channels, and hidden features for effective multivariate time-series modeling. It also supports various attention mechanisms starting from simple gated attention to more complex self-attention blocks that can be customized accordingly. The model can be pretrained and subsequently used for various downstream tasks such as forecasting, classification and regression.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。