PID制御に着想を得たニューラルネット時系列予測の精度向上手法：水道需要予測への適用
Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction
PID制御の考え方を応用し、ニューラルネットによる多段階時系列予測の各ステップの予測値を補正することで、モデルを複雑にせず予測精度を高める手法を提案し、水道需要と電力消費の予測で有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Tony Salloom, Okyay Kaynak, Xinbo Yub, Wei He
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
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.
