TSMixer: An all-MLP Architecture for Time Series Forecasting

Real-world time-series datasets are often multivariate with complex dynamics. Commonly-used high capacity architectures like recurrent- or attention-based sequential models have become popular. However, recent work demonstrates that simple univariate linear models can outperform those deep alternatives. In this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), an architecture designed by stacking multi-layer perceptrons (MLPs). TSMixer is based on mixing operations along time and feature dimensions to extract information efficiently. On popular academic benchmarks, the simple-to-implement TSMixer is comparable to specialized state-of-the-art models that leverage the inductive biases of specific benchmarks. On the challenging and large scale M5 benchmark, a real-world retail dataset, TSMixer demonstrates superior performance compared to the state-of-the-art alternatives. Our results underline the importance of efficiently utilizing cross-variate and auxiliary information for improving the performance of time series forecasting. The design paradigms utilized in TSMixer are expected to open new horizons for deep learning-based time series forecasting.

Interpretable Sequence Learning for COVID-19 Forecasting

We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance. Explainability is valuable to ensure that the model's forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions. Our model can be applied at different geographic resolutions, and here we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts, in metrics averaged across the entire US, than state-of-the-art alternatives, and that it provides qualitatively meaningful explanatory insights. Lastly, we analyze the performance of our model for different subgroups based on the subgroup distributions within the counties.