Prediction of US Stocks Based on ARIMA Model

DOI

10.2991/978-94-6463-142-5_35How to use a DOI?

Keywords

ARIMA; Stock price forecast

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.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Boyu Xiao
PY  - 2023
DA  - 2023/05/15
TI  - Prediction of US Stocks Based on ARIMA Model
BT  - Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023)
PB  - Atlantis Press
SP  - 312
EP  - 322
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-142-5_35
DO  - 10.2991/978-94-6463-142-5_35
ID  - Xiao2023
ER  -