Commodity futures are volatile. Forecasting across horizons with interpretable drivers remains challenging. We propose the Sparse Latent Factor Forecaster with Iterative Inference (SLFF), a structured prediction latent variable model that combines sparse coding, unrolled optimization, and amortized inference. SLFF explicitly optimizes a sparse latent code to explain multi-horizon futures trajectories and trains an encoder whose outputs are validated against the optimization-based solution before deployment. The method is paired with an information set aware pipeline (vintage macro releases, lag aware fills, leakage checks) and evaluated under rolling origin folds against representative statistical and neural baselines. We provide quantitative criteria for factor labeling and directional diagnostics that account for no change regimes. On Copper and WTI futures (2005-2023), SLFF achieves competitive RMSE and MAE, improves directional skill beyond persistence, and yields factors that are stable across seeds and linked to measurable fundamentals. Code, diagnostics, and information set specifications are released for reproducibility.

翻译：商品期货价格波动剧烈。在多个预测期限上实现具有可解释驱动因素的预测仍具挑战性。本文提出一种结合迭代推理的稀疏潜在因子预测器（SLFF），这是一个融合稀疏编码、展开式优化与摊销推理的结构化预测潜变量模型。SLFF显式地优化一个稀疏潜在编码，以解释多期限期货价格轨迹，并训练一个编码器，其输出在部署前需通过基于优化的解进行验证。该方法与一个信息集感知处理流程（包含宏观数据发布时点、滞后感知填充与泄露检查）相结合，并通过滚动原点交叉验证与代表性统计及神经基线模型进行比较评估。我们提出了因子标记的量化标准与考虑无变化区间的方向性诊断指标。在铜和WTI期货（2005-2023年）数据上，SLFF取得了具有竞争力的RMSE与MAE指标，其方向预测能力超越持续性基准，并产生跨随机种子稳定且与可观测基本面相关联的因子。为促进可复现性，我们公开了代码、诊断工具及信息集配置规范。