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.

多元时间序列预测是机器学习领域的一个重要领域，该研究提出了一种名为神经预测层 (NFCL) 的模型，通过神经网络的简单融合，不受其他输入的干扰，实现了透明的预测结果解释。实证研究结果表明，NFCL 在 15 个开放数据集上胜过 9 种基准模型，不仅预测性能优秀，而且能够提供预测解释。通过多种模型结构的严格实验证明了 NFCL 独特配置的合理性。