Statistical Comparison ARIMA Order Performance In Stock Market
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85185837464
Journal Title
7th International Conference on Information Technology, InCIT 2023
Start Page
81
End Page
85
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SCOPUS
Bibliographic Citation
7th International Conference on Information Technology, InCIT 2023 (2023) , 81-85
Suggested Citation
Deemee C., Ngampis K., Noraset T., Thaipisutikul T., Sun M.T., Kitchat K. Statistical Comparison ARIMA Order Performance In Stock Market. 7th International Conference on Information Technology, InCIT 2023 (2023) , 81-85. 85. doi:10.1109/InCIT60207.2023.10413152 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97434
Title
Statistical Comparison ARIMA Order Performance In Stock Market
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Abstract
Stock market forecasting is important for financial decision-making and risk management. Among the various time series models, the Autoregressive (p) Integrated (d) Moving Average (q) (ARIMA) model has been widely adopted for its simplicity and effectiveness in capturing temporal patterns. However, selecting appropriate ARIMA orders remains a crucial and challenging task, impacting the accuracy of predictions. This paper presents a comprehensive statistical comparison of ARIMA order performance in the context of stock market forecasting. We examine the impact of different ARIMA model orders. Our study utilizes historic New York Stock Exchange (NYSE) stock price data. Our findings shed light on the complex interplay between ARIMA parameters and predictive accuracy, offering valuable insights for robust financial forecasting.