Demand forecasting predicts a future quantity using information available at prediction time. Evaluate in time order and compare with a simple historical baseline.
Before you begin
Know Python, tabular data handling and basic statistics. Use fictional or permitted data and identify the prediction or evaluation task.
Work through the idea
Keep an experiment record with the dataset, split, baseline, metric and limitations. Choose information that would genuinely be available at the time of prediction. Compare the method with a simple alternative and inspect mistakes. A learning experiment should not be presented as a validated production decision system.
- Define forecast horizon
- Separate training and future periods
- Consider seasonality and missing observations
Practice task
Forecast fictional weekly demand and compare against the previous week's demand.
Check your result
Explain the error metric, stockout-related limitations and one period of failure.
A mistake to avoid
Do not randomly mix future observations into training for a time-based forecast.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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