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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2021
EditorsBudi Purnama, Dewanta Arya Nugraha, A. Suparmi
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735441859
DOIs
Publication statusPublished - 24 Mar 2022
Event2021 International Conference on Science and Applied Science, ICSAS 2021 - Surakarta, Virtual, Indonesia
Duration: 6 Apr 20216 Apr 2021

Publication series

NameAIP Conference Proceedings
Volume2391
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2021 International Conference on Science and Applied Science, ICSAS 2021
Country/TerritoryIndonesia
CitySurakarta, Virtual
Period6/04/216/04/21

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