Climate modeling using vector moving average autoregressive

Abstract

Information on weather and climate is a major need to support the smooth running of activities in various sectors, one of which is the agricultural sector. The agricultural sector is very dependent on climate change, where extreme climate change can cause storms, floods, droughts, resulting in crop failure. Therefore, climate information is very important, with information on climate change predictions, it can be used to determine when the right planting time is so that the quality and yield of agricultural production increases. The purpose of this study is to model climate data rainfall, humidity, maximum temperature, maximum temperature, and the length of solar radiation using a Vector Autoregressive Integrated Moving Average (VARIMA) approach. VARIMA is a time series model involving multivariate time series data, where the VARIMA model is a vector form of the ARIMA model. The time series analysis model requires data to meet stationary requirements. The results of the analysis obtained stationary climate data after being differed once, so the differencing data results that will be used in modelling. The time series climate data model was combined until the fourth lag based on SAS software assistance and the results were obtained namely the VARIMA model (1,1,1) with MSE values of 6045,33 and AICC namely 17,97823 and VARIMA (2,1,0) with MSE values namely 7432,434 and AICC which is 17,978023. Based on the two models, one of the best models was chosen with the smallest MSE and AIC value criteria. Thus the best VARIMA model that can be used to predict climate data in Merauke is the VARIMA (1,1,1) model.