PatchTSMixer: Lightweight Time-Series Modeling Approach
Patchmixer introduces PatchTSMixer, a cutting-edge approach to time-series modeling built upon the MLP-Mixer architecture. This HuggingFace implementation of PatchTSMixer offers a seamless integration of lightweight mixing capabilities across patches, channels, and hidden features, revolutionizing multivariate time-series analysis. The model boasts support for diverse attention mechanisms, ranging from basic gated attention to intricate self-attention blocks, allowing for adaptable customization. With pretraining functionality and versatility in downstream applications like forecasting, classification, and regression, PatchTSMixer stands as a game-changer in the realm of time-series data analysis and prediction.