Assessment Effects of Data Preprocessing and Modeling Parameters of Gene Expression Programming on Accuracy of Time Series Forecasting

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SALEHI M.; FATEMI S.E.

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Journal Paper

Paper Information

Journal: Iranian Journal of Irrigation and Drainage Year:2021 | Volume:15 | Issue:3 Page(s): 582-597

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Information Journal Paper

Title

Assessment Effects of Data Preprocessing and Modeling Parameters of Gene Expression Programming on Accuracy of Time Series Forecasting

Author(s)

SALEHI M. | FATEMI S.E. | Issue Writer Certificate

Keywords

Forecasting

Gene Expression

Periodicity

Preprocessing

Time Series

Abstract

Hydrological time-series is a time-dependent hydrological variable that finding the model of changes and predicting is the most important goal of time-series analysis. The purpose of this study is to simultaneously study the characteristics of Time Series and their prediction and the important parameters of the GEP for high-precision predictions in the training and validation. In this study, groundwater depth time-series of Chamchamal plain station located in Kermanshah province with a 12-year period and mountainous climate and the monthly time-series of Alaska temperature with a 50-year period and cold and dry climate have been used. Genexprotools5. 0 software has been used to model time-series by GEP. The results of studying with GEP showed that the Periodicity of data properties that existed in the Time Series of temperature caused correlation results above 90% in different stages of training and validation. So that the effect of different parameters of GEP is less than 10% in improving results. On the other hand, by examining the time-series of groundwater depth, which lacks Periodicity and has a descending ACF shape, the prediction results of the GEP with any effective expression parameter, R more than 44% in the validation wasn't obtained. This means that the time-series Preprocessing has a greater impact on the prediction results. So that by eliminating the semester, the prediction results in all stages of modeling are significantly reduced. In this case, the best R for the validation is 50%.

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APA: Copy

SALEHI, M., & FATEMI, S.E.. (2021). Assessment Effects of Data Preprocessing and Modeling Parameters of Gene Expression Programming on Accuracy of Time Series Forecasting. IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, 15(3 ), 582-597. SID. https://sid.ir/paper/1054211/en

Vancouver: Copy

SALEHI M., FATEMI S.E.. Assessment Effects of Data Preprocessing and Modeling Parameters of Gene Expression Programming on Accuracy of Time Series Forecasting. IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE[Internet]. 2021;15(3 ):582-597. Available from: https://sid.ir/paper/1054211/en

IEEE: Copy

M. SALEHI, and S.E. FATEMI, “Assessment Effects of Data Preprocessing and Modeling Parameters of Gene Expression Programming on Accuracy of Time Series Forecasting,” IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, vol. 15, no. 3 , pp. 582–597, 2021, [Online]. Available: https://sid.ir/paper/1054211/en

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