Application and Improvement Analysis of the ARIMA Model in the Financial Field

DOI

10.2991/978-94-6463-748-9_23How to use a DOI?

Keywords

Time Series Models; ARIMA Model; Optimization

Abstract

This paper discusses the application and optimization methods of time series models in the financial field, focusing on the effectiveness of Autoregressive Integrated Moving Average (ARIMA) models and their hybrid models in actual cases. The prediction results of several hybrid models selected in this paper are observed to be better than the original models. First, this paper uses the Exponential Smoothing-Artificial Neural Network (ETS-ANN) model to predict the European cryptocurrency market during the COVID-19 pandemic and finds that the model can keenly obtain the characteristics of trends and seasonal changes. Due to emergencies, such as the COVID-19 pandemic, there may be a lack of training data, resulting in unclear features. The ETS-ANN model can effectively avoid this problem. In addition, to be more in line with the actual financial market, this paper selects a long-term forecast case and the Autoregressive Integrated Moving Average-Symmetric Generalized Auto Regressive Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-SGARCH) model increases the ability to handle volatility aggregation. This paper also compares other models of the GARCH family, which can be used for further optimization. Finally, this paper combines artificial neural networks with ARIMA models, selects the case of forecasting the exchange rate between the Malaysian ringgit and the US dollar, and promotes the use of bootstrap and double bootstrap methods to reduce errors.

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  - Haomiao Xu
PY  - 2025
DA  - 2025/07/03
TI  - Application and Improvement Analysis of the ARIMA Model in the Financial Field
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 194
EP  - 201
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_23
DO  - 10.2991/978-94-6463-748-9_23
ID  - Xu2025
ER  -