Version 1
: Received: 28 October 2024 / Approved: 29 October 2024 / Online: 30 October 2024 (10:38:57 CET)
How to cite:
Brown, M.; Chen, E.; Lee, S.; Taylor, J.; Kim, A.; Johnson, R. Comparative Analysis of LSTM and Traditional Time Series Models on Oil Price Data. Preprints2024, 2024102355. https://doi.org/10.20944/preprints202410.2355.v1
Brown, M.; Chen, E.; Lee, S.; Taylor, J.; Kim, A.; Johnson, R. Comparative Analysis of LSTM and Traditional Time Series Models on Oil Price Data. Preprints 2024, 2024102355. https://doi.org/10.20944/preprints202410.2355.v1
Brown, M.; Chen, E.; Lee, S.; Taylor, J.; Kim, A.; Johnson, R. Comparative Analysis of LSTM and Traditional Time Series Models on Oil Price Data. Preprints2024, 2024102355. https://doi.org/10.20944/preprints202410.2355.v1
APA Style
Brown, M., Chen, E., Lee, S., Taylor, J., Kim, A., & Johnson, R. (2024). Comparative Analysis of LSTM and Traditional Time Series Models on Oil Price Data. Preprints. https://doi.org/10.20944/preprints202410.2355.v1
Chicago/Turabian Style
Brown, M., Anna Kim and Robert Johnson. 2024 "Comparative Analysis of LSTM and Traditional Time Series Models on Oil Price Data" Preprints. https://doi.org/10.20944/preprints202410.2355.v1
Abstract
Long Short-Term Memory (LSTM) networks have gained attention for their forecasting capabilities, especially in volatile markets like oil. This study compares LSTM networks with traditional time series models such as ARIMA and Exponential Smoothing in predicting oil prices. We analyze a comprehensive dataset of historical oil price movements and apply preprocessing techniques to ensure the reliability of our model training and evaluation processes. The models are evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to measure their forecasting performance. The results demonstrate that LSTM networks significantly outperform traditional time series models, effectively capturing price trends and fluctuations. Additionally, LSTM shows enhanced adaptability to sudden changes in the market, while traditional models tend to struggle in such scenarios. This comparative analysis underscores the significance of model selection in time series forecasting, especially for industries characterized by high volatility, and suggests a shift towards utilizing LSTM for enhanced financial forecasting accuracy. Future research may further investigate hybrid models that combine the strengths of both LSTM and traditional methodologies.
Keywords
ARIMA; LSTM; Oil Prices Prediction
Subject
Computer Science and Mathematics, Other
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.
