Application of the Time Series Model in Forecasting Stock Trends

DOI

10.2991/978-94-6463-748-9_30How to use a DOI?

Keywords

Time Series Analysis; Stock Price Prediction; ARIMA

Abstract

As financial management becomes more technically sophisticated, the prediction of stock price becomes a significant task for investors and traders. The autoregressive integrated moving average (ARIMA) model is a technique commonly employed to predict trends in stock prices. Moreover, it is a time series model, which is more advanced to provide an appropriate rate of daily return with confidence intervals. This paper will utilize the ARIMA model to estimate the share price of Amazon. The stock data for period from January 1, 2020 to April 30, 2020 is chosen, and it includes vital information - the starting price. The research findings indicate that the ARIMA model can capture the dynamic trends of stock prices to a certain extent. The value of MAPE is 2.0088% which is much less than 10%, indicating that the result of the prediction is good. Meanwhile, the valuable reference information it provided for investors and traders can help them better understand the market direction and potential risks.

Copyright

© 2025 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  - Jiayu Dai
PY  - 2025
DA  - 2025/07/03
TI  - Application of the Time Series Model in Forecasting Stock Trends
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 266
EP  - 273
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_30
DO  - 10.2991/978-94-6463-748-9_30
ID  - Dai2025
ER  -