Latest   Machine Learning Plug-and-Play Reinforcement Learning for Real-Time Forecast Recalibration 0 like May 12, 2025 Share this post Last Updated on May 12, 2025 by Editorial Team Author(s): Shenggang Li Originally published on Towards AI. Updating legacy ARMA sales models with a PPO residual corrector — no full retrain requiredPhoto by Anders Jildén on Unsplash When I build a time-series model — say an ARMA trained on last season’s prices, promos, and holiday flags — to forecast daily sales, everything looks sharp on the validation plots. A few months later the marketing team flips its playbook, competitors slash tags, and the once-neat residuals drift off-center. Many supply-chain teams shrug, dump in the new data, and grind through a full retrain: pick fresh lags, rerun grid-search, redeploy the pipeline. That rebuild cycle is slow, breaks governance checkpoints, and constantly resets alert thresholds. Worse, every restart discards the hard-won structure already baked into the original ARMA. So this paper asks a simpler question: Why not keep the trusted core and bolt on a reinforcement-learning auto-tuner? The RL agent observes yesterday’s forecast error together with live context (today’s price, promo flag, competitor price, marketing spend), then nudges the baseline up or down by a modest percentage — think of a thermostat giving the heater tiny extra bursts when a cold front hits. We train that agent with Proximal Policy Optimization (PPO). PPO treats each correction as a continuous action, rewards relative error reduction, and clips policy steps so the tweak never jumps wildly. By learning online it… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post