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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Forecasting commodity prices: empirical evidence using deep learning tools.

Hachmi Ben Ameur¹, Sahbi Boubaker², Zied Ftiti³

¹INSEEC Grande Ecole, Omnes Education, Paris, France.

Annals of Operations Research

|January 30, 2023

Summary

This summary is machine-generated.

This study shows Long Short-Term Memory deep learning is effective for commodity price forecasting. The Livestock and Industrial Metals Subindices are superior for assessing other commodity indices, aiding risk management.

Keywords:

Bloomberg Commodity Index Commodity markets Deep learning Forecasting Performance metrics

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Area of Science:

* Financial Markets and Operational Research
* Artificial Intelligence (AI) and Data Science

Background:

* Financial markets have transformed due to crises, increasing interest in alternative assets like commodities.
* Advancements in AI, including machine learning and deep learning, offer new tools for market analysis.
* Algorithm selection in AI is critical; deep learning is applied when machine learning is insufficient.

Purpose of the Study:

* To investigate the effectiveness of deep learning algorithms for forecasting commodity prices.
* To analyze the Bloomberg Commodity Index and its five subindices (Agriculture, Precious Metals, Livestock, Industrial Metals, Energy).
* To evaluate forecasting performance using daily data from January 2002 to December 2020.

Main Methods:

* Utilized deep learning algorithms for time-series forecasting.
* Employed the Bloomberg Commodity Index and its component subindices.
* Analyzed daily data spanning nearly two decades (2002-2020).

Main Results:

* The Long Short-Term Memory (LSTM) method demonstrated significant effectiveness in commodity price forecasting.
* The Bloomberg Livestock Subindex and Bloomberg Industrial Metals Subindex proved superior for assessing other commodity indices.
* Findings highlight the practical utility of specific deep learning models and indices.

Conclusions:

* Deep learning, particularly LSTM, is a valuable tool for commodity price forecasting.
* Specific subindices offer superior insights for broader commodity market assessment.
* Results provide crucial information for investor risk management and public policy adjustments, especially during geopolitical events.