关键词: 天然气收益率预测, 动态时间规划算法, 支持向量机, 模拟退火算法

Abstract: This paper, based on Americas natural gas price from 2000 to 2018, establishes DTW-SVM-SA(dynamic time warping-support vector machine-simulated annealing) combined model to forecast the natural gas price. It also compares the DTM-SVM-SA modeling results with comparison modeling results at different intervals, and evaluates its forecasting accuracy and error. Simulated annealing algorithm can optimize the free parameters of SVM and weight parameters of the combined model. DTM-SVM-SA model shows a good generalized capacity in forecasting natural gas yield, with outstandingly rising accuracy at difference intervals and decreasing error, which is an effective gas price forecasting model. DTW-SVM-SA model can not only provide references for governmental macroscopic controls, but also help enterprises better forecast and manage risks in price changes, especially for energy enterprises.

Key words: natural gas yield forecast, dynamic time warping, support vector machine, simulated annealing algorithm