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Time Series Analysis in the Prediction of Water Quality

机译：水质预测时序列分析

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The time series analysis method and the actual situation of the real-time monitoring system of the coastal waters are used to select the real-time monitoring data of the dissolved oxygen (DO) in the water quality monitoring data from 24th to 28th March 2016 as a research sample, the ARIMA model was fitted in the Eviews. The model was used to predict the data on March 29th and 30th and compared with the actual measured data. The results show that the relative error between the predicted value and the real value is 4-12% and the average error is about 4.79%. It is proved that the time series analysis is good enough in the water quality prediction. By comparing the results of static prediction and dynamic prediction, it is discussed that the limitation and future research of time series analysis method in water quality prediction problem.
机译：沿海水域实时监测系统的时间序列分析方法和实际情况用于从2016年3月24日至28日的水质监测数据中的溶解氧（DO）的实时监测数据研究样本，Arima模型安装在Eviews中。该模型用于预测3月29日和30日的数据，并与实际测量数据进行比较。结果表明，预测值与实际值之间的相对误差为4-12％，平均误差约为4.79％。事实证明，时间序列分析在水质预测中足够好。通过比较静态预测和动态预测的结果，探讨了时间序列分析方法在水质预测问题中的限制和未来研究。

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