  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.
