What are the most effective ways to validate forecasting models?

1 Use historical data

One of the simplest and most common ways to validate forecasting models is to use historical data. This means comparing your forecasted values with the actual values that occurred in the past, and measuring the error or deviation between them. Historical data can help you assess how well your models capture the patterns, trends, and seasonality of your demand, and how sensitive they are to changes and outliers. You can use various metrics to quantify the error, such as mean absolute error (MAE), mean squared error (MSE), or mean absolute percentage error (MAPE).

2 Apply cross-validation

Another way to validate forecasting models is to apply cross-validation. This means splitting your data into different subsets, such as training, validation, and test sets, and using them to fit, tune, and evaluate your models respectively. Cross-validation can help you avoid overfitting or underfitting your models, which can lead to poor generalization and performance. You can use different methods of cross-validation, such as holdout, k-fold, or leave-one-out, depending on the size and characteristics of your data.

3 Perform backtesting

A third way to validate forecasting models is to perform backtesting. This means simulating how your models would have performed in the past, using the data that was available at that time, and comparing the results with the actual outcomes. Backtesting can help you test the robustness and stability of your models, and how they react to different scenarios and events. You can use different techniques of backtesting, such as rolling window, expanding window, or walk-forward, depending on the frequency and horizon of your forecasts.

5 Solicit feedback

A fifth way to validate forecasting models is to solicit feedback. This means involving other stakeholders, such as sales, marketing, or operations teams, and asking them to review and comment on your forecasts and models. Soliciting feedback can help you incorporate qualitative and contextual information that may not be captured by quantitative data, and improve your understanding of your market and customer behavior. You can use different tools and platforms to facilitate feedback, such as surveys, dashboards, or collaborative software.