Implementation of Auto ARIMA, PSO-LSTM, and PSO-GRU for Time Series Modeling of 3 Telecommunication Company Stock Prices LQ45 Index

Abstract

The Indonesia Stock Exchange (IDX) issues stock indices to make it easier for investors to choose company shares such as the LQ45 Index. This study focuses on forecasting the share prices of 3 telecommunications companies listed in the LQ45 Index, namely PT Telkom Indonesia Tbk with the stock code TLKM, PT Tower Bersama Infrastructure Tbk with the stock code TBIG and PT Sarana Menara Nusantara Tbk with the stock code TOWR in the future. The algorithms used for forecasting are Auto ARIMA, LSTM and GRU algorithms. In addition, the PSO method is used to find the optimal hyperparameters in the LSTM and GRU algorithms. The results of this study show that the GRU model has the best performance and produces the best model evaluation value compared to other models on TLKM and TBIG stock data, while on TOWR stock data the LSTM model is the best model. The GRU model on TLKM data results in an R Square value of 0,961, RMSE 122,291 on training data and MAPE 3,027% and an R Square value of 0,859, RMSE 114,703 and 2,109% on testing data. On TBIG data, the GRU model results in an R Square value of 0,984, RMSE 71,945 and MAPE 4,206% on training data and R Square an value of 0,967, RMSE 73,627 and 2,165% on testing data. The LSTM model on TOWR data results in an R Square value of 0,943, RMSE 43,824 and MAPE 4,274% on training data and an R Square value of 0,796, RMSE 42,597 and 3,117% on testing data.