Pemodelan Runtun Waktu Harga Saham Bulanan BBRI.JK dengan Metode MODWT-ARIMA

Abstract

MODWT-ARIMA is a time series modeling that combines the MODWT process and the ARIMA process. The MODWT process is used as pre-processing data while the ARIMA process as a time series modeling for data from MODWT decomposition. This study aims to show that time series modeling with a combined MODWT-ARIMA process provides more accurate forecast result compared to the ARIMA model. The modeled data is  monthly time period data of BBRI’s stock price started from January 2018 to July 2018. Accuracy measurement of the forecasting result is based on the RMSE value. The result is the MODWT-ARIMA model has a RMSE value  which is smaller than the ARIMA model with RMSE  Forecasting value using MODWT-ARIMA method for the period January 2018 to July 2018 are, 3687,560, 3571,892,  3287,686, 3072,610, 2832,533, 3147,472, 2964,491. The result of diagnostic checking from ARIMA model for D2, D3, and S3, shows that the residual model is not white noise while of the ARIMA model for the time series of monthly stock prices show thet the residual model is white noise. Theoritically, a model that has no white noise’s residual is considered to be less able to describe the properties of the observed data and further residual modeing should be done. However, this research is sufficient for the ARIMA model and it turns out that it has been able to show that the MODWT-ARIMA model is more effective than the ARIMA model.