Version 1
: Received: 28 October 2024 / Approved: 28 October 2024 / Online: 28 October 2024 (13:58:39 CET)
How to cite:
Xu, Q.; Wang, T.; Cai, X. Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI. Preprints2024, 2024102161. https://doi.org/10.20944/preprints202410.2161.v1
Xu, Q.; Wang, T.; Cai, X. Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI. Preprints 2024, 2024102161. https://doi.org/10.20944/preprints202410.2161.v1
Xu, Q.; Wang, T.; Cai, X. Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI. Preprints2024, 2024102161. https://doi.org/10.20944/preprints202410.2161.v1
APA Style
Xu, Q., Wang, T., & Cai, X. (2024). Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI. Preprints. https://doi.org/10.20944/preprints202410.2161.v1
Chicago/Turabian Style
Xu, Q., Tangtang Wang and Xiaowei Cai. 2024 "Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI" Preprints. https://doi.org/10.20944/preprints202410.2161.v1
Abstract
The volatility of global energy markets, particularly electricity prices, plays a crucial role in influencing international economic activities. With the ongoing global energy transition and the push for low-carbon development, predicting electricity prices has become increasingly important for policymakers and market participants. This paper explores the forecasting capabilities of the ARIMA and LSTM models in analyzing electricity prices in the United States, drawing from data spanning 2001 to 2024.ARIMA, a traditional time series model, is valued for its simplicity and effectiveness in capturing linear trends, while LSTM, a deep learning-based model, excels at handling long-term dependencies in complex datasets. This study reveals that while both models offer valuable insights, each exhibits limitations. ARIMA struggles with non-linear patterns and volatility, whereas LSTM tends to underestimate extreme price values. The findings highlight the potential of hybrid models that combine traditional and machine learning approaches to enhance forecasting accuracy in the increasingly dynamic energy market. This research provides essential guidance for improving decision-making processes in the context of the global shift towards clean energy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
