Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices

Andrew Nilsen; Bevina Desjwiandra H.; Gatot Fatwanto Hertono

doi:10.1063/5.0073838

Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices

Andrew Nilsen, Bevina Desjwiandra H., Gatot Fatwanto Hertono

Department of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Investing in the stock market is not easy, because the stock price always fluctuates. These fluctuations can lead to losses if the prediction is wrong. Therefore, we need a method that can predict the direction of stock movements. This paper presents a comparative study for stock price prediction using Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model. For building and testing the models, the historical values of PT Bank Central Asia Tbk (IDX: BBCA) stock closing price and PT Kertas Tjiwi Kimia Tbk (IDX: TKIM) stock closing price for the period 01-January-2015 to 01-January-2020, were used. The result shows that the Gated Recurrent Unit (GRU) model has better accuracy than the Autoregressive Integrated Moving Average (ARIMA) model.

Original language	English
Title of host publication	International Conference on Science and Applied Science, ICSAS 2021
Editors	Budi Purnama, Dewanta Arya Nugraha, A. Suparmi
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735441859
DOIs	https://doi.org/10.1063/5.0073838
Publication status	Published - 24 Mar 2022
Event	2021 International Conference on Science and Applied Science, ICSAS 2021 - Surakarta, Virtual, Indonesia Duration: 6 Apr 2021 → 6 Apr 2021

Publication series

Name	AIP Conference Proceedings
Volume	2391
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2021 International Conference on Science and Applied Science, ICSAS 2021
Country/Territory	Indonesia
City	Surakarta, Virtual
Period	6/04/21 → 6/04/21

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10.1063/5.0073838

Cite this

Nilsen, A., H., B. D., & Hertono, G. F. (2022). Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices. In B. Purnama, D. A. Nugraha, & A. Suparmi (Eds.), International Conference on Science and Applied Science, ICSAS 2021 [020004] (AIP Conference Proceedings; Vol. 2391). American Institute of Physics Inc.. https://doi.org/10.1063/5.0073838

Nilsen, Andrew ; H., Bevina Desjwiandra ; Hertono, Gatot Fatwanto. / Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices. International Conference on Science and Applied Science, ICSAS 2021. editor / Budi Purnama ; Dewanta Arya Nugraha ; A. Suparmi. American Institute of Physics Inc., 2022. (AIP Conference Proceedings).

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title = "Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices",

abstract = "Investing in the stock market is not easy, because the stock price always fluctuates. These fluctuations can lead to losses if the prediction is wrong. Therefore, we need a method that can predict the direction of stock movements. This paper presents a comparative study for stock price prediction using Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model. For building and testing the models, the historical values of PT Bank Central Asia Tbk (IDX: BBCA) stock closing price and PT Kertas Tjiwi Kimia Tbk (IDX: TKIM) stock closing price for the period 01-January-2015 to 01-January-2020, were used. The result shows that the Gated Recurrent Unit (GRU) model has better accuracy than the Autoregressive Integrated Moving Average (ARIMA) model. ",

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Nilsen, A, H., BD & Hertono, GF 2022, Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices. in B Purnama, DA Nugraha & A Suparmi (eds), International Conference on Science and Applied Science, ICSAS 2021., 020004, AIP Conference Proceedings, vol. 2391, American Institute of Physics Inc., 2021 International Conference on Science and Applied Science, ICSAS 2021, Surakarta, Virtual, Indonesia, 6/04/21. https://doi.org/10.1063/5.0073838

Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices. / Nilsen, Andrew; H., Bevina Desjwiandra ; Hertono, Gatot Fatwanto.

International Conference on Science and Applied Science, ICSAS 2021. ed. / Budi Purnama; Dewanta Arya Nugraha; A. Suparmi. American Institute of Physics Inc., 2022. 020004 (AIP Conference Proceedings; Vol. 2391).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Investing in the stock market is not easy, because the stock price always fluctuates. These fluctuations can lead to losses if the prediction is wrong. Therefore, we need a method that can predict the direction of stock movements. This paper presents a comparative study for stock price prediction using Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model. For building and testing the models, the historical values of PT Bank Central Asia Tbk (IDX: BBCA) stock closing price and PT Kertas Tjiwi Kimia Tbk (IDX: TKIM) stock closing price for the period 01-January-2015 to 01-January-2020, were used. The result shows that the Gated Recurrent Unit (GRU) model has better accuracy than the Autoregressive Integrated Moving Average (ARIMA) model.

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Nilsen A, H. BD , Hertono GF. Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices. In Purnama B, Nugraha DA, Suparmi A, editors, International Conference on Science and Applied Science, ICSAS 2021. American Institute of Physics Inc. 2022. 020004. (AIP Conference Proceedings). doi: 10.1063/5.0073838

Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Gated Recurrent Unit (GRU) model in predicting stock prices

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