The key idea

A backtest asks what a model would have predicted at past decision dates and compares those predictions with subsequent results. For inventory planning, replay the relevant replenishment horizon and stock policy. The test must not use information from after the decision date, even when that information is now easy to access.

Scikit learn documents chronological splitting for time ordered data. The inventory evaluation workflow is our own operational recommendation. [1]

Build a chronological evaluation

Choose several historical cutoffs. At each cutoff, fit or update the model using earlier observations, create its forecast and hold the later outcome aside. Preserve source timestamps for external signals. A recently retrieved history can contain revisions that were not known at the original date, so document what is and is not a genuine as of replay. Keep the test sample consistent when comparing a baseline and an Amazon assisted model.

Translate forecasts into the same decision rules

For each forecast, apply the same lead time, stock caps and batch rules. Compare likely shortages and excess units, not only the distance between two lines. Use actual receipts and orders when they are available, but label simulated supply assumptions. The result should identify where the new method helps and where it remains weak. One aggregate improvement should not hide a large deterioration for the titles with the highest financial exposure.

Worked example · illustrative assumptions

Suppose a publisher chooses twelve weekly decision dates and a seven day horizon. For the first date, only earlier sales and signals are allowed. The next seven days become the test outcome. Repeat that process at each date, then compare the baseline and candidate on the same title set. If one source has only four weeks of trustworthy history, report that shorter evidence window instead of inventing the missing eight weeks.

Put it into practice

  • Fix cutoffs, horizons and eligible titles before comparing results.
  • Use only information available at each historical decision time.
  • Report forecast error alongside simulated inventory consequences.

Watch out for

A random train and test split can mix later observations into the learning process for earlier decisions. That is not a convincing replay of how a publisher would have operated through time.

Does a successful backtest guarantee future performance?

No. It provides evidence under historical conditions. Continue monitoring live outcomes because campaigns, assortment and fulfillment processes can change after the evaluation period.

Sources and further reading

  1. scikit-learn: TimeSeriesSplit. Chronological validation rather than training on future observations.

AI assisted editorial content with original planning examples. Source links support the stated source notes; operational suggestions are guidance rather than guarantees. Provider rules and product coverage can change. Editorial approach · BooksAhead.ai connection status.