FORECASTING SAHAM SYARIAH DENGAN MENGGUNAKAN LSTM

AHMAD FAUZI

Abstract


Islamic stocks as one of the many stocks listed on the JCI are a barometer of the Islamic market in Indonesia. One approach in predicting stock prices is by using machine learning. The purpose of this study is to make a model that is used to predict JII shares using the LSTM approach. The data used amounted to 1402 records related to the Jakarta Islamic Index (JII) stock from March 4, 2014 - January 2, 2019. Model making uses 3 Epochs (1, 10 and 20). The results showed the best model was to use 20 Epochs. The increase in Epoch affects the value  of MSE and RMSE which are getting smaller. For Epoch 20, the values of MSE and RMSE are ~ 0.00019 and~0.014, respectively.

Keywords


Forecasting, LSTM, Islamic Stock

References


Brockwell, P. J., Davis, R. A., & Calder, M. V. (2002). Introduction to Time series and Forecasting. New York: Springer.

Cao, J., Li, Z., & Li, J. (2019). Financial Time Series Forecasting Model Based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and its Applications, 519: 127-139.

Cao, L. J., & Tay, F. E. H. (2003). Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Transactions on Neural Networks, 14(6): 1506-1518.

Kim, K. J. (2003). Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing, 55(1-2): 307-319.

Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression. Decision Support Systems, 47(2): 115-125.

Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and DataDriven Decision Making. Big data, 1(1): 51-59.

Tay, F. E., & Cao, L. (2001). Application of Support vector Machines in Financial Time Series Forecasting. Omega, 29(4): 309-317.




DOI: http://dx.doi.org/10.15548/al-masraf.v4i1.210

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