Time Series Forecasting of Temperatures using SARIMA: An Example from Nanjing

Abstract

Time series modelling and forecasting – a method that predicts future values by analysing past values - plays an important role in many practical fields. In this paper, we analyse the monthly mean temperature in Nanjing, China, from 1951 to 2017, using SARIMA (Seasonal Autoregressive Integrated Moving Average) techniques. Data from 1951 to 2014 are used as the training set, while data from 2015 to 2017 are used as the testing set. A detailed explanation of model selection and forecasting accuracy is presented. The results show that the proposed research approach obtains good forecasting accuracy.