关键词: 时空预测, 差分自回归移动平均（ARIMA）模型, 多元线性回归（MLR）

Abstract:

A prediction method utilizing ARIMA based on spatio-temporal Multiple Linear Regression（MLR） is proposed, referring to the problems of ignoring space influence and space-time interaction in traditional time series modeling and prediction. The method is applied to spatio-temporal prediction of monthly average temperature in Liaoning Province. Firstly, the obvious seasonality existing in the space-time variable is removed by time series decomposition. Secondly, the MLR model for the Seasonality-Removed Data（SRD） is fitted by all-subsets regression method, in order to obtain the space-time trend in the SRD. Thirdly, an ARIMA model is fitted for the random data of each station. Finally, the space-time estimations are composed of the random estimations and other ones of first two steps. The experimental results show that the correlation coefficient between estimation and observation values is 0.993 4 and the mean absolute error is about 0.9 ℃.

Key words: spatio-temporal prediction, ARIMA model, Multiple Linear Regression（MLR）