Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used to accomplish this step. However, the complexity of the neural network structure still stands up as a critical problem against prediction accuracy. In this paper, a method inspired by the proportional-integral-derivative (PID) control approach is investigated to enhance the performance of neural network models used for multi-step ahead prediction of periodic time-series information while maintaining a negligible impact on the complexity of the system. The PID-based method is applied to the predicted value at each time step to bring that value closer to the real value. The water demand forecasting problem is considered as a case study, where two deep neural network models from the literature are used to prove the effectiveness of the proposed boosting method. Furthermore, to prove the applicability of this PID-based booster to other types of periodic time-series prediction problems, it is applied to enhance the accuracy of a neural network model used for multi-step forecasting of hourly energy consumption. The comparison between the results of the original prediction models and the results after using the proposed technique demonstrates the superiority of the proposed method in terms of prediction accuracy and system complexity.

翻译：多步时序预测是多个工业领域中决策者所需的关键支持步骤。近年来，采用神经网络组件的人工智能技术已频繁用于实现这一步骤。然而，神经网络结构的复杂性仍是影响预测精度的关键问题。本文研究了一种受比例-积分-微分（PID）控制方法启发的技术，旨在提升用于周期性时序信息多步超前预测的神经网络模型性能，同时保持对系统复杂性的可忽略影响。该基于PID的方法在每个时间步长对预测值进行调整，使其更接近真实值。研究以需水量预测问题作为案例，采用文献中的两种深度神经网络模型验证所提增强方法的有效性。此外，为证明该基于PID的增强器适用于其他类型的周期性时序预测问题，将其应用于提升用于小时能耗多步预测的神经网络模型精度。原始预测模型与采用所提技术后结果的对比表明，该方法在预测精度和系统复杂性方面均具有优越性。