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[Time series] Add PatchTSMixer #26247
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. |
@amyeroberts - Greetings! We have enabled all the changes suggested in PatchTST and also the corrections suggested in this PR from the past reviewers. Requesting your review and approval. |
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Thanks this looks a lot better than last time. Left some small nits but should be good to go otherwise
forecast_channel_indices (`list`, *optional*): | ||
List of channel indices to forecast. If None, forecast all channels. Target data is expected to have all | ||
channels and we explitly filter the channels in prediction and target before loss computation. | ||
num_targets (`int`, *optional*, defaults to 3): |
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still not resolved
Co-authored-by: Arthur <[email protected]>
Co-authored-by: Arthur <[email protected]>
Co-authored-by: Arthur <[email protected]>
Hi @ArthurZucker -Thanks for approving this PR. We have resolved all the final changes you mentioned. Please review and help with merge, if all good. PS: change with respect to adding segment names to docstring is the only one comment pending. When we add segment names in docstring following the syntax ssuggested - it getting auto removed during make fix-ups. Other than this - all other comments are resolved. |
@vijaye12 thanks for bearing with us in this long review! and congrats for the merge! 🚀 |
PatchTSMixer (KDD 2023) 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.
@kashif
Done:
TODOs