PatchTST model pre trained on ETTh1 dataset PatchTST is a transformer based model for time series modeling tasks, including forecasting, regression, and classification. This repository contains a pre trained PatchTST model encompassing all seven channels of the ETTh1 dataset. This particular pre trained model produces a Mean Squared Error (MSE) of 0.3881 on the test split of the ETTh1 dataset when forecasting 96 hours into the future with a historical data window of 512 hours. For training and evaluating a PatchTST model, you can refer to this demo notebook. Model Details Model Description The PatchTST model was proposed in A Time Series is Worth 64 Words: Long term Forecasting with Transformers by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. At a high level the model vectorizes time series into patches of a given size and encodes the resulting sequence of vectors via a Transformer that then outputs the prediction length forecast via an appropriate head. The model is based on two key components: (i) segmentation of time series into subseries level patches which are served as input tokens to Transformer; (ii) channel independence where each channel contains a sing…
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