Abstract
The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.
Highlights
The hybrid method is a method of combining two forecasting models
linear model will be combined with the Autoregressive Integrated Moving Average
then the residual value of the Time Series Regression (TSR) linear model will be modeled by the Autoregressive Integrated Moving Average (ARIMA) model
Summary
1.1 Latar Belakang TSR linier merupakan model peramalan yang menggunakan variabel dependen dan variabel independen dalam pemodelan. Model TSR linier dapat digunakan pada data yang memiliki pola trend maupun seasonal [2]. Model ARIMA adalah model peramalan yang tidak mensyaratkan suatu pola data tertentu. Pemodelan dan peramalan harga saham yang akurat diperlukan sebagai dasar dalam pengambilan keputusan investasi. Telkom Indonesia Tbk menggunakan model hybrid TSR linier-ARIMA. Terdapat dua model peramalan kuantitatif, yaitu model runtun waktu (time series) dan model regresi (regression) [1]. Terdapat perbedaan diantara keduanya, yaitu pada TSR variabel dependen dan variabel independennya merupakan runtun waktu. Dengan Yt adalah variabel dependen atau data pengamatan model TSR periode ke-t, Tt adalah komponen trend, St adalah komponen seasonal, dan et adalah residual periode ke- t.
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