Informer2020
Informer significantly enhances long sequence time-series forecasting by optimizing transformer efficiency with ProbSparse Attention. This method focuses on active queries, resulting in superior predictions. The project includes thorough experiment reproducibility utilizing datasets like ETTh1 and ETTh2, and operates on PyTorch. Core features encompass model training commands, data integration, and optimization settings. Informer efficiently manages large data sequences, essential for AI-based predictions across diverse applications. Frequent updates ensure it remains at the forefront of AI research.
