ACADSTAFF UGM
| Title | : | Long Short-Term Memory Neural Network Model for Time Series Forecasting: Case Study of Forecasting IHSG during Covid-19 Outbreak |
| Author | : |
RADEN AURELIUS ANDHIKA VIADINUGROHO (1) Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2) |
| Date | : | 19 2021 |
| Keyword | : | LSTM; ARIMA; Covid 19 LSTM; ARIMA; Covid 19 |
| 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. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
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