Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (12:29:33 CEST)
How to cite:
Sun, J.; Zhou, S.; Zhan, X.; Wu, J. Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques. Preprints2024, 2024090983. https://doi.org/10.20944/preprints202409.0983.v1
Sun, J.; Zhou, S.; Zhan, X.; Wu, J. Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques. Preprints 2024, 2024090983. https://doi.org/10.20944/preprints202409.0983.v1
Sun, J.; Zhou, S.; Zhan, X.; Wu, J. Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques. Preprints2024, 2024090983. https://doi.org/10.20944/preprints202409.0983.v1
APA Style
Sun, J., Zhou, S., Zhan, X., & Wu, J. (2024). Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques. Preprints. https://doi.org/10.20944/preprints202409.0983.v1
Chicago/Turabian Style
Sun, J., Xiaoan Zhan and Jiang Wu. 2024 "Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques" Preprints. https://doi.org/10.20944/preprints202409.0983.v1
Abstract
Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.
Keywords
Time Series Analysis; Machine Learning Models; Deep Learning Forecasting; Supply Chain; LSTM
Subject
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.
