Decathlon swapped weekly DeepAR retraining for a six-month fine-tune of Chronos-2

Start with the retraining cadence, because that is where the operational bill changes. DeepAR was retrained every week specifically to adapt to recent trend shifts ^[8]. Chronos-2 is fine-tuned once every six months ^[16], which is roughly 26 weeks between weight refreshes ^[22]. That moves the job of tracking a trend break out of the training loop and into the context the model is handed at inference time. Under the old design, a demand shift in March was in the weights by the following Monday. Under the new one, it has to be read off the recent history of the series. That is a reasonable trade if the model reads context well, and it is the behaviour I would regression-test hardest before putting it in front of purchase planners.

The second structural change is the seam. From 2021 to 2024 the stack ran DeepAR for weeks 1 to 16 and Holt-Winters for weeks 17 to 52 ^[8], so the 12-week replenishment forecast that feeds supplier orders ^[4] sat entirely inside the deep model, and the 52-week capacity view ^[5] came from something else. Covariates only enter the story in 2024, with Temporal Fusion Transformer for long-horizon accuracy ^[9]. One model now spans both horizons at weekly frequency ^[13].

The evaluation is the part worth copying. Week 48 of 2022 to week 44 of 2024 is about 100 weeks, and Decathlon ran 101 rolling cutoffs across it ^[11], so the cutoffs sit one week apart and the backtest replicates the production cadence rather than sampling convenient dates ^[21]. About 25,000 products appear per cutoff against 39,000 unique series over the full window ^[12]. That difference is roughly 14,000 series entering or leaving, about 56 percent of a single cutoff's panel ^[20]. Catalogue churn on that scale is exactly why the zero-shot result matters: new products arrive with no history, and weights refreshed twice a year will not have seen them ^[15]. At least five named supply zones at up to 25,000 products each is up to 125,000 series per weekly run ^[23].

For the numbers to transfer, your baseline has to look like theirs. Decathlon's production comparator was a global deep model retrained weekly ^[8], not a per-SKU statistical fit, so a shop still running per-item exponential smoothing is not reading its own bake-off here. WAPE is volume-weighted ^[14], which means high-volume lines dominate the headline figure and a long tail of single-store items can be badly wrong without moving it. Weekly frequency, strong seasonality across more than 80 sports ^[18], and horizons of 12 and 52 weeks are the shape of the workload the result was measured on.

The AWS post says fine-tuned Chronos-2 beat every other evaluated model at both horizons and that fine-tuning cut error by several percentage points ^[1][24], but the model-by-model figures sit in Decathlon's own analysis on Medium ^[17]. For a decision that ends in supplier orders, that is one hop further from the evidence than I would like.