Use ARIMA (Autoregressive Integrated Moving Average) Model to Study the Future Development Trend of Google’s Stock

DOI

10.2991/978-94-6463-748-9_64How to use a DOI?

Keywords

Time Series Model; ARIMA Model; Google’s Stock

Abstract

This study using the data from June 20, 2024, to December 19, 2024 Google, investigates the application of the AMIRA model to predict future trends in Google’s stock prices. Google, a subsidiary of Alphabet Inc., is a global technology leader whose stock performance reflects market dynamics influenced by innovation and advertising revenue. Traditional statistical models often struggle with the complexity of stock data, which includes non-linearity and volatility. To address these challenges, this study employed the ARIMA (2,2,1) model, it can better predict this volatility data. The research involved testing stationarity( the ADF (Augmented Dickey-Fuller) test and the white noise test), determining model parameters through ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) analyses, and evaluating model performance using metrics like AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and residual analysis. As result, the results demonstrate that the ARIMA model effectively captures trends in Google’s stock prices, with a Mean Absolute Percentage Error (MAPE) of less than 1%. The prediction accuracy is very high. This study highlights the advantages of the ARIMA model in accurately predicting future volatility data, emphasizing its potential for broader applications in the financial sector.

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  - Zimeng Wang
PY  - 2025
DA  - 2025/07/03
TI  - Use ARIMA (Autoregressive Integrated Moving Average) Model to Study the Future Development Trend of Google’s Stock
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 568
EP  - 578
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_64
DO  - 10.2991/978-94-6463-748-9_64
ID  - Wang2025
ER  -