Long Short-Term Memory Neural Network Model for Time Series Forecasting: Case Study of Forecasting IHSG during Covid-19 Outbreak
Penulis/Author
RADEN AURELIUS ANDHIKA VIADINUGROHO (1); Prof. Dr.rer.nat. Dedi Rosadi, S.Si.,, M.Sc. (2)
Tanggal/Date
19 2021
Kata Kunci/Keyword
Abstrak/Abstract
Long Short-Term Memory (LSTM) is one of the developments from Recurrent Neural
Network (RNN) architecture. In this paper, we use LSTM architecture for modeling and
forecasting the Indonesian Composite Stock Price Index (IHSG) closing value data. We also
compare the performance of the LSTM method with the ARIMA and the Radial Basis Function
(RBF) Neural Network method. In the implementation, we use both R and Python open source
software. For empirical study we use the data from January until August 2020 to see the
performance of the considered methods during Covid-19 pandemic periods of time. From the
analysis, we found that LSTM performs better than ARIMA, but outperformed by RBF for this
data.
