Multiclass Forecasting on Panel Data Using Autoregressive Multinomial Logit and C5.0 Decision Tree

Abstract

Panel data is commonly used for the numerical response variables, while the literature for forecasting categorical variables on the panel data structure is still challenging to find. Forecasting is important because it is helpful for government policies. This study aimed to forecast multiclass or categorical variables on the panel data structure. The proposed forecasting models were autoregressive multinomial logit and autoregressive C5.0. The strategy applied so that the two models could be used for forecasting was to add autoregressive effects and fixed predictor variables such as location, time, strata, and month of observations. The autoregressive effect  was assumed to be a fixed effect and treated as a dummy variable. The data used was the category of land conditions through The Area Sampling Frame (ASF) survey conducted by the BPS-Statistics Indonesia. The evaluation of both models was based on classification and forecasting performance. Classification performance was obtained by dividing the dataset into 75% training data for modeling and 25% test data for validation and then repeated 200 times. The classification results showed that the autoregressive C5.0 accuracy was 86.48%, while the autoregressive multinomial logit was 83.97%. A comparison of forecasting performance was obtained by dividing the data into training and testing based on the time sequence. The result showed that the forecasting performance was worse than the classification performance. Autoregressive C5.0 had an accuracy of 77.43%, while autoregressive multinomial logit had 77.77%.