NFCL: Simply interpretable neural networks for a short-term multivariate forecasting
This addresses the need for interpretable models in time-series forecasting, offering a solution that balances accuracy with explainability, though it appears incremental in its approach.
The paper tackles the problem of interpretability in multivariate time-series forecasting by proposing the Neural ForeCasting Layer (NFCL), which uses a simple neural network integration to provide transparent explanations for predictions while achieving superior performance over nine benchmark models on 15 datasets.
Multivariate time-series forecasting (MTSF) stands as a compelling field within the machine learning community. Diverse neural network based methodologies deployed in MTSF applications have demonstrated commendable efficacy. Despite the advancements in model performance, comprehending the rationale behind the model's behavior remains an enigma. Our proposed model, the Neural ForeCasting Layer (NFCL), employs a straightforward amalgamation of neural networks. This uncomplicated integration ensures that each neural network contributes inputs and predictions independently, devoid of interference from other inputs. Consequently, our model facilitates a transparent explication of forecast results. This paper introduces NFCL along with its diverse extensions. Empirical findings underscore NFCL's superior performance compared to nine benchmark models across 15 available open datasets. Notably, NFCL not only surpasses competitors but also provides elucidation for its predictions. In addition, Rigorous experimentation involving diverse model structures bolsters the justification of NFCL's unique configuration.