IEEE

A Hybrid Approach for Time Series Forecasting Using Temporal Convolution Networks and CatBoost Classifier

Abstract: In retail, demand forecasting is crucial for managing inventory, enhancing efficiency, and ensuring customer satisfaction. Traditional methods often fail to capture complex data patterns, ...
GitHub

Hierarchical Patch Based Transformer with Learnable Weighted Loss Optimization for Multivariate Time Series Forecasting (HPBTF)

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 ...
today.ucsd

Scripps Oceanography Seismologist Awarded for Real-Time Tsunami Forecast Breakthrough

A team of researchers from The University of Texas at Austin (UT), Lawrence Livermore National Laboratory (LLNL), and Scripps Institution of Oceanography at UC San Diego has won the 2025 Association ...
IEEE

Time-Series Forecasting of Energy Consumption Using LSTM Networks for Optimized Microgrid Management

Abstract: Accurate energy consumption forecasting is crucial for the efficient management of microgrids, especially as renewable energy sources become increasingly integrated into power systems. In ...