Practical insights

Results

18 results (published only)

• Time series and forecasting

  Report error by horizon and evaluate at the horizon the decision uses

  A dashboard shows one pooled error for a 1-to-90-day forecast that feeds pricing decisions made 21 days before departure.

  easydocumented fact

• Time series and forecasting

  Compute WAPE at the level where actuals cannot sum to zero

  WAPE is reported for every store-SKU-week cell and many cells are blank or infinite.

  easypractitioner heuristic

• Time series and forecasting

  Do not average MAPE across series of very different volumes

  A portfolio report averages per-series MAPE, and a few slow movers make the whole forecast look broken.

  easydocumented fact

• Time series and forecasting

  Model one-off events as interventions and keep them out of learned seasonality

  A city hosted a single major sports final, and hotel and flight demand spiked for one week.

  easydocumented fact

• Time series and forecasting

  Shift before rolling so window features exclude the target period

  A 7-day rolling mean of sales is computed and joined to the same row as the target.

  easydocumented fact

• Time series and forecasting

  Prefer simple models on very short series and say how little the backtest can tell you

  A new route has 30 weeks of history, and someone proposes a seasonal ARIMA with many parameters.

  easydocumented fact

• Time series and forecasting

  Measure forecast value added at every step, including human overrides

  Planners adjust the statistical forecast every month, and nobody knows whether the adjustments help.

  easypractitioner heuristic

• Time series and forecasting

  Track forecast bias separately from accuracy

  A forecast with acceptable MAE consistently runs below actual demand on high-season weeks.

  easypractitioner heuristic

• Time series and forecasting

  Build an explicit time grid so missing periods are not mistaken for zeros or skipped

  A route's daily table has a nine-day gap where no rows exist, and the lag-7 feature is computed with row offsets.

  easypractitioner heuristic

• Time series and forecasting

  End each backtest origin on a complete aggregation period

  Daily bookings are rolled up to weeks, and the cutoff falls on a Wednesday; the training week's total contains three days and the test week begins mid-week.

  easypractitioner heuristic

• Time series and forecasting

  Build the naive benchmark at the production horizon from the origin value, not from the previous day

  A 28-day-ahead model is compared with a naive baseline computed as yesterday's actual for every target day.

  easydocumented fact

• Time series and forecasting

  Retire sMAPE from forecast reports because of its asymmetry and its failure at zero

  A report uses symmetric MAPE because it is bounded and the team believes it fixes MAPE's bias toward low forecasts.

  easydocumented fact

• Time series and forecasting

  Put a sampling error on measured interval coverage before calling a model miscalibrated

  An 80% interval covered 74% of 50 backtest origins and the team wants to retune the model.

  easypractitioner heuristic

• Time series and forecasting

  Report how many series the new model beats the benchmark on, next to the average improvement

  A candidate model improves average MASE over 2,000 series by 4% and the team proposes a full cutover.

  easypractitioner heuristic

• Time series and forecasting

  Take the seasonal difference first and let a seasonal-strength measure decide whether you need one

  Daily bookings show a strong weekly pattern and a trend, and an analyst applies a first difference and then wonders why the weekly spikes are still there.

  easydocumented fact

• Time series and forecasting

  Let a damped trend compete with a linear trend whenever automatic forecasts run beyond a few periods

  A monthly forecast for 1,000 route-level series is produced automatically, and a linear-trend Holt model projects 24 months of continued growth on routes that recently grew.

  easydocumented fact

• Time series and forecasting

  Convert monthly totals to per-day or per-trading-day rates before modelling seasonality

  Monthly revenue for a retailer swings partly because months contain different numbers of trading days, and the seasonal model learns a short-February dip and a long-month bump as seasonality.

  easydocumented fact

• Time series and forecasting

  Check that the method tolerates gaps, and model systematic closures with dummies rather than interpolating them

  A store's daily sales are missing on public holidays when it is closed, and the pipeline either fails inside an exponential smoothing fit or silently interpolates the closures.

  easydocumented fact