NFCL: Simply interpretable neural networks for a short-term multivariate forecasting

TL;DR

NFCL introduces a simple, interpretable neural network model for multivariate time-series forecasting that outperforms benchmarks and provides transparent explanations for its predictions.

Contribution

The paper presents NFCL, a novel neural network architecture that is both effective and inherently interpretable for short-term multivariate forecasting.

Findings

NFCL outperforms nine benchmark models on 15 datasets.

NFCL offers transparent explanations for its forecasts.

Empirical results validate NFCL's superior performance and interpretability.

Abstract

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.