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Indonesian Stock Prices Prediction using Bidirectional Long Short-Term Memory

Authors:

I Gede Angga Dinata,

Novanto Yudistira,

Lailil MuflikhahAuthors Info & Claims

SIET '22: Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology

Pages 188 - 198

https://doi.org/10.1145/3568231.3568249

Published: 13 January 2023 Publication History

Abstract

This paper aims to know how well Bidirectional Long Short-Term Memory (BiLSTM) is in predicting Indonesian stock prices. First, the best hyperparameter of BiLSTM is searched through hyperparameter tuning. After finding the best hyperparameter, we train the data train containing close prices from Indonesian stock. After that, we try to find optimal days to predict by measuring the error rate using Mean Absolute Error (MAE). We also compared BiLSTM with Recurrent Neural Network (RNN) and LSTM by comparing the MAE from each method. Finally, we also tried using multivariate BiLSTM using Indonesian stock. The evaluations yield the best hyperparameters setting and how many days suitable for predicting BiLSTM performance. BiLSTM performed better than RNN and LSTM. Moreover, univariate BiLSTM performs better than multivariate BiLSTM in predicting Indonesian stock prices.

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Cited By

Zhu RSong XZhang HCai X(2024)Joint Extraction of Entity Relationships in Walnut Disease and Pest Based on Chinese NLP Models2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)10.1109/EEBDA60612.2024.10485759(1027-1035)Online publication date: 27-Feb-2024
https://doi.org/10.1109/EEBDA60612.2024.10485759

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SIET '22: Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology

November 2022

398 pages

ISBN:9781450397117

DOI:10.1145/3568231

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 January 2023

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SIET '22

SIET '22: 7th International Conference on Sustainable Information Engineering and Technology 2022

November 22 - 23, 2022

Malang, Indonesia

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Cited By

Zhu RSong XZhang HCai X(2024)Joint Extraction of Entity Relationships in Walnut Disease and Pest Based on Chinese NLP Models2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)10.1109/EEBDA60612.2024.10485759(1027-1035)Online publication date: 27-Feb-2024
https://doi.org/10.1109/EEBDA60612.2024.10485759

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