Abstract: Due to building thermal inertia and delayed user behavioral responses, power load often lags behind meteorological changes, particularly drops in temperature and humidity. Existing models ...
Spatio-temporal forecasting is often used in traffic and weather forecasting, and it adds a spatial dimension compared to univariate and multivariate forecasting. In spatio-temporal forecasting, if ...
Abstract: Deep learning models employing the Transformer architecture have demonstrated exceptional performance in the field of multivariate time series forecasting research. However, these models ...
This code is the official PyTorch implementation of our NIPS'25 paper: Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective. If you find this project helpful, ...
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