TimeMixer provides a PyTorch implementation of a time series forecasting architecture that decomposes temporal patterns across multiple scales and mixes them for accurate predictions. The model leverages decompose-then-forecast mechanisms to capture both short-term and long-term temporal dependencies. This repository includes training scripts, evaluation benchmarks, and the complete model architecture.