Content area
Full text
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.
1. Introduction
The global and regional climates have already begun changing [1], and as the important factor of climate change, temperature plays a significant role in human’s daily life [2, 3]. It is important to forecast extreme temperature accurately [4]. Temperature change process is usually nonlinear, complex, and dynamic, so the accurate prediction of extreme temperature is faced with a high degree of scientific uncertainty, which traditional deterministic mathematical model cannot solve perfectly. And numerical simulation method can solve the problem better [4–6].
Auto regression (AR) model is the traditional method used to deal with the time series forecasting [7]; for example, Bańbura et al. used Bayesian vector autoregressions for commercial forecasting [8]. In recent years, artificial neural network (ANN) algorithms are widely used to deal with forecasting meteorological objects [9]. Based on the genetic algorithm (GA) and particle swarm algorithm, Yang designed the Back-Propagation (BP) neural networks to establish the multifactor time series forecasting model [10]. At the same time, set pair analysis (SPA) model which is easy to operate and gives good prediction results is also popular in meteorological forecast field [5, 6]. Yang et al. gave the set pair analysis based on similarity forecast (SPA-SF) model for forecasting water resources changing process, and the application results showed that the statistic and physical concepts of SPA-SF were distinct and its precision was high [9]. Recently, Mei et al. used SPA to find an optimal choice of Bioretention media [11] and Guo et al. employed it to assess the ecoenvironment quality for uncertain problems [12]. Because SPA model does...