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Decoding Futures Price Dynamics: A Regularized Sparse Autoencoder for Interpretable Multi-Horizon Forecasting and Factor Discovery

Abhijit Gupta

TL;DR

This paper addresses the challenge of forecasting commodity futures across multiple horizons while uncovering interpretable market drivers. It introduces the Regularized Sparse Autoencoder (RSAE), a three-component, energy-based framework that enforces sparsity on the latent code $\mathbf{z}$ via $L1$ regularization to jointly predict future prices and discover latent factors, using amortized inference for test-time predictions. Evaluated on Copper and Crude Oil data from 2005–2023, RSAE delivers competitive multi-horizon forecasting accuracy and yields latent factors that align with economic drivers such as demand, supply shocks, USD strength, and sentiment. The approach offers a transparent alternative to black-box models, with potential benefits for risk management and scenario analysis through interpretable, data-driven latent factors.

Abstract

Commodity price volatility creates economic challenges, necessitating accurate multi-horizon forecasting. Predicting prices for commodities like copper and crude oil is complicated by diverse interacting factors (macroeconomic, supply/demand, geopolitical, etc.). Current models often lack transparency, limiting strategic use. This paper presents a Regularized Sparse Autoencoder (RSAE), a deep learning framework for simultaneous multi-horizon commodity price prediction and discovery of interpretable latent market drivers. The RSAE forecasts prices at multiple horizons (e.g., 1-day, 1-week, 1-month) using multivariate time series. Crucially, L1 regularization ($\|\mathbf{z}\|_1$) on its latent vector $\mathbf{z}$ enforces sparsity, promoting parsimonious explanations of market dynamics through learned factors representing underlying drivers (e.g., demand, supply shocks). Drawing from energy-based models and sparse coding, the RSAE optimizes predictive accuracy while learning sparse representations. Evaluated on historical Copper and Crude Oil data with numerous indicators, our findings indicate the RSAE offers competitive multi-horizon forecasting accuracy and data-driven insights into price dynamics via its interpretable latent space, a key advantage over traditional black-box approaches.

Decoding Futures Price Dynamics: A Regularized Sparse Autoencoder for Interpretable Multi-Horizon Forecasting and Factor Discovery

TL;DR

This paper addresses the challenge of forecasting commodity futures across multiple horizons while uncovering interpretable market drivers. It introduces the Regularized Sparse Autoencoder (RSAE), a three-component, energy-based framework that enforces sparsity on the latent code via regularization to jointly predict future prices and discover latent factors, using amortized inference for test-time predictions. Evaluated on Copper and Crude Oil data from 2005–2023, RSAE delivers competitive multi-horizon forecasting accuracy and yields latent factors that align with economic drivers such as demand, supply shocks, USD strength, and sentiment. The approach offers a transparent alternative to black-box models, with potential benefits for risk management and scenario analysis through interpretable, data-driven latent factors.

Abstract

Commodity price volatility creates economic challenges, necessitating accurate multi-horizon forecasting. Predicting prices for commodities like copper and crude oil is complicated by diverse interacting factors (macroeconomic, supply/demand, geopolitical, etc.). Current models often lack transparency, limiting strategic use. This paper presents a Regularized Sparse Autoencoder (RSAE), a deep learning framework for simultaneous multi-horizon commodity price prediction and discovery of interpretable latent market drivers. The RSAE forecasts prices at multiple horizons (e.g., 1-day, 1-week, 1-month) using multivariate time series. Crucially, L1 regularization () on its latent vector enforces sparsity, promoting parsimonious explanations of market dynamics through learned factors representing underlying drivers (e.g., demand, supply shocks). Drawing from energy-based models and sparse coding, the RSAE optimizes predictive accuracy while learning sparse representations. Evaluated on historical Copper and Crude Oil data with numerous indicators, our findings indicate the RSAE offers competitive multi-horizon forecasting accuracy and data-driven insights into price dynamics via its interpretable latent space, a key advantage over traditional black-box approaches.
Paper Structure (44 sections, 4 figures, 8 tables)

This paper contains 44 sections, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Review of Related Work in Commodity Forecasting
  3. Introduction to Commodity Price Forecasting: Significance and Challenges
  4. Traditional Econometric Approaches: Foundations and Limitations
  5. Rise of Machine Learning in Commodity Forecasting
  6. Key Machine Learning Techniques Applied
  7. Methodology: The Regularized Sparse Autoencoder (RSAE) Framework
  8. Problem Formulation
  9. Model Architecture
  10. Latent Variable Framework: Energy Function and Inference
  11. Selection and Convergence of Inference Steps (K)
  12. Test-Time Inference
  13. Training Objective and Parameter Updates
  14. Overall Training Algorithm
  15. Interpretability via Sparsity
  16. ...and 29 more sections

Figures (4)

  • Figure 1: Conceptual architecture of the Regularized Sparse Autoencoder (RSAE) framework.
  • Figure 2: Mean objective energy function value $E(Y, \bm{h}, \bm{z})$ versus the number of inference iterations ($k$), averaged over representative validation set batches. The plot demonstrates typical convergence behavior, with energy decreasing rapidly initially before plateauing. The selected $K=10$ (dashed red line) represents a point of diminishing returns for energy reduction.
  • Figure 3: Heatmap of Correlations: Copper Latent Factors vs. Selected Features (Test Set).
  • Figure 4: Heatmap of Correlations: Crude Oil Latent Factors vs. Selected Features (Test Set).