ARIMA-M: A New Model for Daily Water Consumption Prediction Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction
Hongyan Du, Zhihua Zhao, Huifeng Xue
Abstract
Water resource is considered as a significant factor in the development of regional environment and society. Water consumption prediction can provide an important decision basis for the regional water supply scheduling optimizations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model was proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points were used to verify the effectiveness of the model, and the future water consumption was predicted in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.