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 ...

A) Retail/E-commerce inventory (forecasting product demand for stores or online sales) B) Manufacturing raw materials (forecasting material needs for production) C) Distribution/logistics (forecasting ...

Abstract: Deep learning models employing the Transformer architecture have demonstrated exceptional performance in the field of multivariate time series forecasting research. However, these models ...

Forecasting, a fundamental task in machine learning, involves predicting future values of a time series based on its historical behavior. This paper introduces a novel Hierarchical Patch Based ...