Fast and interpretable time series forecasting using long short-term cognitive networks

Long Short-term Cognitive Networks (LSTCNs) are recurrent neural networks for efficient forecasting of univariate and multivariate time series. They consist of a sequential ensemble of Short-term Cognitive Networks (STCNs), each processing a time patch and passing knowledge to the next block. Originally designed to handle long time series, the LSTCNs’ performance on shorter, real-world time series remains unexplored. Besides their efficiency, LSTCNs are deemed interpretable to some extent since both neural concepts and weights have a precise meaning in the domain-specific problem. However, determining feature importance goes beyond inspecting the learned weights and requires more advanced post-hoc methods that operate on the network’s internal representations. In this paper, we present two main contributions that address these experimental and theoretical research gaps. As the first contribution, we propose the Sparseness-Optimized Feature Importance for Time Series Forecasting (SOFI-TSF) explainer to rank neural concepts in LSTCN models according to their relevance for the forecast. While this explainer can be applied to any forecaster, our proposal includes knowledge-specific methods to initialize the feature ranking to be improved by SOFI-TSF’s optimizer. As a second contribution, besides performing a hyperparameter sensitivity analysis, we conducted an extensive comparative analysis contrasting LSTCNs’ performance against other forecasters based on recurrent neural networks, transformers, and hierarchical interpolation. In these studies, we used 25 real-world time series datasets at different prediction horizons and the M5 forecasting competition dataset. The simulations show that (i) all SOFI-TSF variants tested in our simulations largely outperform the baselines in terms of fidelity and explanation sparsity, (ii) LSTCNs outperform the other recurrent forecasters in terms of both forecasting error and runtime.