Plug-and-Play Reinforcement Learning for Real-Time Forecast Recalibration

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.

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Published via Towards AI