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[2505.06795] Sparse Latent Factor Forecaster (SLFF) with Iterative Inference for Transparent Multi-Horizon Commodity Futures Prediction
Summary
The Sparse Latent Factor Forecaster (SLFF) proposes a new approach for predicting commodity futures by addressing forecast errors and enhancing interpretability through iterative inference and sparse coding.
Why It Matters
This research is significant as it tackles the common challenges of forecast accuracy and interpretability in machine learning models used for commodity futures prediction. By improving the forecasting process, the SLFF model could lead to better decision-making in financial markets, which is crucial for investors and analysts.
Key Takeaways
- SLFF improves forecast accuracy for commodity futures over traditional neural models.
- The model utilizes a sparse coding objective and iterative inference to refine predictions.
- Interpretability is enhanced through a structured protocol assessing stability and validity.
- Empirical results show significant performance gains at 1- and 5-day prediction horizons.
- The research includes practical implementations with released code and data artifacts.
Computer Science > Machine Learning arXiv:2505.06795 (cs) [Submitted on 11 May 2025 (v1), last revised 15 Feb 2026 (this version, v5)] Title:Sparse Latent Factor Forecaster (SLFF) with Iterative Inference for Transparent Multi-Horizon Commodity Futures Prediction Authors:Abhijit Gupta View a PDF of the paper titled Sparse Latent Factor Forecaster (SLFF) with Iterative Inference for Transparent Multi-Horizon Commodity Futures Prediction, by Abhijit Gupta View PDF HTML (experimental) Abstract:Amortized variational inference in latent-variable forecasters creates a deployment gap: the test-time encoder approximates a training-time optimization-refined latent, but without access to future targets. This gap introduces unnecessary forecast error and interpretability challenges. In this work, we propose the Sparse Latent Factor Forecaster with Iterative Inference (SLFF), addressing this through (i) a sparse coding objective with L1 regularization for low-dimensional latents, (ii) unrolled proximal gradient descent (LISTA-style) for iterative refinement during training, and (iii) encoder alignment to ensure amortized outputs match optimization-refined solutions. Under a linearized decoder assumption, we derive a design-motivating bound on the amortization gap based on encoder-optimizer distance, with convergence rates under mild conditions; empirical checks confirm the bound is predictive for the deployed MLP decoder. To prevent mixed-frequency data leakage, we introduce an inform...
