Predicting and forecasting of time series models using cluster analysis

Abstract

Time series model on multiple objects could be univariate and multivariate model. The more objects used in multivariate model would decrease precision of forecast for each object. One way to overcome this problem used univariate models for each object. However, univariate models for each object became inefficient in time. Therefore, clustering performed on objects so that model became efficient. The objective of this research is to study results of predicting and forecasting model with and without clustering. Model and forecasting used time series regression model on broad proportion of plant-disturbing organism attack and planting area of food crops in Indonesia. Clustering of time series data was same as clustering in general, but the distance and method should be able to accommodate time series data structure which was dynamic in time. Evaluation of prediction and forecast mean average percentage error (MAPE) show that forecast of model was performed with clustering as good as forecast of model for each object. However, prediction of model with clustering was not as good as the prediction of model for each object so that the prediction of broad proportion of plant-disturbing organism attack only served as an indicator of the arrival populations of plant-disturbing organism.