TSMixer: An All-MLP Architecture for Time Series Forecasting

Understand

Real-world time-series datasets are often multivariate with complex dynamics.

• To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning models have become popular.
• However, recent work demonstrates that simple univariate linear models can outperform such deep learning models on several commonly used academic benchmarks.
• Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons (MLPs).