SpectraNet: multivariate forecasting and imputation under distribution shifts and missing data

Neural forecasting has become an active research area, with advancements in architectural design steadily improving performance and scalability. Most existing approaches produce forecasts using a fixed parametric function with historical values as inputs. We identify performance limitations of this approach in handling two recurrent challenges in IoT data: distribution shifts and missing data. We propose SpectraNet, a model based on a new paradigm for time-series forecasting. We introduce a latent factor inference method that matches the model's output on past observations. Theoretically motivated as a MAP estimation of the posterior distribution of latent factors, the inference process provides additional flexibility to adjust forecasts based on the latest information. We identify three advantages of our method: (i) SoTA performance with 92% fewer parameters and similar training times; (ii) superior robustness to missing data and distribution shifts; and (iii) capability to simultaneously produce forecasts and interpolate past missing data, unifying imputation and forecasting tasks.