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Mathematical Problems in Engineering
Volume 2015, Article ID 385876, 4 pages
Research Article

Wavelet Network Model Based on Multiple Criteria Decision Making for Forecasting Temperature Time Series

School of Environmental Sciences, Beijing Normal University, Beijing 100875, China

Received 24 October 2014; Revised 18 January 2015; Accepted 25 January 2015

Academic Editor: Hector Puebla

Copyright © 2015 Jian Zhang et al. 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.


Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regression (NNBR), the probability of relative error smaller than 10% increases from 65.79% to 84.21% (forecast period ) and from 51.35% to 91.89% by WNMCDM model. Similarly, the probability of relative error smaller than 20% increases from 84.21% to 97.37% and from 81.08% to 91.89% by WNMCDM model. Therefore, WNMCDM model is superior to NNBR model in forecasting temperature time series.