Prediction of US Stocks Based on ARIMA Model

Boyu Xiao ()
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Boyu Xiao: Guangdong University of Foreign Studies

A chapter in Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), 2023, pp 312-322 from Springer

Abstract: Abstract Time series analysis method is an important part of statistics. It has practical applications in various fields from economics to engineering. Time series analysis includes analyzing time series data in order to extract meaningful features of data and predict future values. Box-Jenkins method belongs to regression analysis method and is the basic method of time series analysis and prediction. This paper describes the modeling method and implementation process of ARIMA. A time series is a series of data points, usually measured at uniform time intervals. Autoregressive integral moving average (ARIMA) model is a kind of linear model that can represent stationary and non-stationary time series. ARIMA model depends on autocorrelation mode to a large extent. This paper will discuss the application in stock price forecasting, especially the time sampling at different time intervals, to determine whether there are some optimal design frameworks and whether the stock autocorrelation patterns in the same industry are similar.

Keywords: ARIMA; Stock price forecast (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-142-5_35

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DOI: 10.2991/978-94-6463-142-5_35

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