Overfitting

As explained in Demand Forecasting Best Practices

Overfitting is a modeling condition in which a model fits historical data well but fails to generalize to future or otherwise unseen data.

Overfitting occurs when a forecasting model fits historical demand unusually well but fails to reproduce that performance on future or otherwise unseen data. It mistakes random variation for a meaningful demand pattern, creating confidence in historical accuracy that does not carry into planning decisions. section 10.1.2 section 14.4.2

The risk appears when a model is selected or optimized against the same historical period used to judge it. A model chosen from many alternatives may win in that period simply because it adapted to its peculiarities, then lose to a benchmark later. Compare forecasts with benchmarks at the same lag, avoid reusing the optimization period for evaluation, and keep tracking both over time. section 10.1.2 section 10.1.2

In demand forecasting, overfitting can result from:

• Using a fixed three-year history when it encourages the model to treat random fluctuations as trends or seasonality. section 14.1.2
• Selecting inadequate parameters or weights, or failing to represent important demand drivers such as prices and promotions. section 14.4.2
• Interpreting strong historical accuracy as evidence that the model will forecast actual future demand well. section 10.1.2

A forecast that wins the past

Suppose a forecasting system chooses one model from 15 candidates because it performs best in 2022. That model will probably beat a benchmark in 2022, but it may perform poorly in 2023 because it learned random features of the earlier period rather than repeatable demand behavior. section 10.1.2

Figure 14.14 Overfitting (left) vs. underfitting (right)

Use the figure to compare the visual signatures of overfitting and underfitting: one captures nonexistent seasonality, while the other misses actual seasonality. section 14.4.2

How Demand Forecasting Best Practices covers it

How deep it goes

Explains causes, benchmark comparisons, data-history risks, and visual contrast with underfitting, but stops short of implementation details.

Examples

Uses a 15-model selection example, three-year historical demand, nonexistent seasonality, and benchmark comparisons across 2022 and 2023.

What it assumes

Assumes familiarity with historical demand, forecasts, benchmarks, and seasonality while defining overfitting through practical supply-chain examples.

What you can do after

Diagnose overfitting by separating optimization from evaluation, matching forecast lags, comparing benchmarks, and monitoring future performance.

How it connects

Connects forecasting, benchmarks, moving averages, time series models, seasonality, demand drivers, machine learning, and underfitting.

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Related concepts

• regularizationaddressed by · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
• dropoutaddressed by · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.
• early stoppingaddressed by · Early stopping is a technique that terminates an iterative process before its planned completion when a specified condition indicates that further work is unnecessary or unlikely to improve the objective.
• underfittingcontrasts with · A modeling failure in which insufficient capacity or training prevents a model from capturing relevant patterns in the data, resulting in poor performance even on training data.
• failure modekind of · A failure mode is a distinct way in which a system, component, process, or operation can fail or produce an undesirable result.
• tree pruningaddressed by · Tree pruning is the removal of decision-tree branches that fail a specified split-quality condition.

This explainer was generated by Manning's knowledge graph from the text of Demand Forecasting Best Practices. The section links point to the parts of the book it draws on.