Applying Shannon Entropy in Bayesian Network Input Preprocessing For Time Series Modeling

Akhoni Pourhosseini, Fateme; Ali Gorbani, Mohammad; Shahedi, Kaka

doi:10.29252/jwmr.9.18.178

Volume 9, Issue 18 (1-2019) jwmr 2019, 9(18): 178-189 | Back to browse issues page

‎ 10.29252/jwmr.9.18.178

‎ 20.1001.1.22516174.1397.9.18.4.8

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Akhoni Pourhosseini F, Ali Gorbani M, Shahedi K. (2019). Applying Shannon Entropy in Bayesian Network Input Preprocessing For Time Series Modeling . jwmr. 9(18), 178-189. doi:10.29252/jwmr.9.18.178
URL: http://jwmr.sanru.ac.ir/article-1-633-en.html

Applying Shannon Entropy in Bayesian Network Input Preprocessing For Time Series Modeling

Fateme Akhoni Pourhosseini

, Mohammad Ali Gorbani

, Kaka Shahedi

Water Resources Engineering, University of Tabriz

Abstract: (3236 Views)

Selecting appropriate inputs for intelligent models is important due to reduce costs and save time and increase accuracy and efficiency of models. The purpose of this study is using Shannon entropy to select the optimum combination of input variables in time series modeling. Monthly time series of precipitation, temperature and radiation in the period of 1982-2010 was used from Tabriz synoptic station. Precipitation, temperature and radiation parameters with different delays are considered as input to the Shannon entropy. The results showed that time series with three delays provide the better results for the modeling. Applying Bayesian network and multivariate linear regression analysis were performed. Models performance was evaluated using three criteria: coefficient of determination (R²), root mean square error (RMSE), and the dispersion. Index (SI). The results indicated that Bayesian neural network model shows the best performance to simulate time series of precipitation, temperature and radiation in compare to multivariate linear regression analysis. The results showed that Shannon entropy has better performance in selection of the appropriate entry into intelligent models.

Keywords: Entropy, Multivariate linear regression, Time series, Bayesian network

Full-Text [PDF 3969 kb] (1194 Downloads)

Type of Study: Research | Subject: Special
Received: 2016/05/31 | Revised: 2019/01/21 | Accepted: 2017/01/25 | Published: 2019/01/21

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