TY - GEN
T1 - Statistical Comparison ARIMA Order Performance In Stock Market
AU - Deemee, Chanatip
AU - Ngampis, Kirati
AU - Noraset, Thanapon
AU - Thaipisutikul, Tipajin
AU - Sun, Min Te
AU - Kitchat, Kotcharat
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - ARIMA model
KW - Historical Stock data
KW - Stock Market
KW - Stock price forecast
KW - Stock price prediction
UR - https://www.scopus.com/pages/publications/85185837464
U2 - 10.1109/InCIT60207.2023.10413152
DO - 10.1109/InCIT60207.2023.10413152
M3 - 會議論文篇章
AN - SCOPUS:85185837464
T3 - 7th International Conference on Information Technology, InCIT 2023
SP - 81
EP - 85
BT - 7th International Conference on Information Technology, InCIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Information Technology, InCIT 2023
Y2 - 15 November 2023 through 17 November 2023
ER -