Working PaperArticleVersion 1This version is not peer-reviewed
ARIMA-M: A New Model for Daily Water Consumption Prediction, Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction
Version 1
: Received: 9 January 2020 / Approved: 10 January 2020 / Online: 10 January 2020 (07:09:20 CET)
How to cite:
Du, H.; Zhao, Z.; Xue, H. ARIMA-M: A New Model for Daily Water Consumption Prediction, Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction. Preprints2020, 2020010095
Du, H.; Zhao, Z.; Xue, H. ARIMA-M: A New Model for Daily Water Consumption Prediction, Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction. Preprints 2020, 2020010095
Cite as:
Du, H.; Zhao, Z.; Xue, H. ARIMA-M: A New Model for Daily Water Consumption Prediction, Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction. Preprints2020, 2020010095
Du, H.; Zhao, Z.; Xue, H. ARIMA-M: A New Model for Daily Water Consumption Prediction, Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction. Preprints 2020, 2020010095
Abstract
Water resource is considered as a significant factor in development of regional environment and society. Water consumption prediction can provide important decision basis for the regional water supply scheduling optimisations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model is 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 are used to verify the effectiveness of the model, and the future water consumption is predicted, in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.
Keywords
water resource management; water consumption prediction; Markov chain; autoregressive moving average model; error correction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
