Forecasting The Broad Proportion Attack of Rice Blast Disease in Indonesia

Abstract

Classical regression analysis is a statistical technique for modeling, forecasting and investigating the relationship between response variable and explanatory variables. However, there are model adequacy must be checked on residual model i.e. autocorrelation. The autocorrelation problem can be solved by modeling the residual of regression model into model that specifically incorporates the autocorrelation structure. Autocorrelation can be caused by residual of regression model increasing over time. The time series regression model is one of the analyzes used to accommodate the model residual which increasing over time. This study used data on the broad proportion of rice blast (Pyricularia grisea) attacks. The purpose of this study is to forecast the broad proportion of rice blast attacks used classical regression model and time series regression model. Evaluate forecast values used mean absolute percentage error (MAPE). The comparison results showed that the forecast of time series regression model better than classical regression model.