PatchTSMixer in HuggingFace - Getting Started
<script async defer src="https://unpkg.com/medium-zoom-element@0/dist/medium-zoom-element.min.js"></script> Open In Colab
PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. It is proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by IBM Research authors Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.
For effective mindshare and to promote open-sourcing - IBM Research joins hands with the HuggingFace team to release this model in the Transformers library.
In the Hugging Face 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.
<script async defer src="https://unpkg.com/medium-zoom-element@0/dist/medium-zoom-element.min.js"></script> Open In Colab
PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. It is proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by IBM Research authors Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.
For effective mindshare and to promote open-sourcing - IBM Research joins hands with the HuggingFace team to release this model in the Transformers library.
In the Hugging Face 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.