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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 T=1) and from 51.35% to 91.89% (T=2) by WNMCDM model. Similarly, the probability of relative error smaller than 20% increases from 84.21% to 97.37% (T=1) and from 81.08% to 91.89% (T=2) by WNMCDM model. Therefore, WNMCDM model is superior to NNBR model in forecasting temperature time series.

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