Forecasting Metrics
Forecasting Metrics groups the core ideas a learner needs at the 9 · evaluation, metrics & diagnostics stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Forecast MAE and RMSEForecast MAE and RMSE is a practical concept within Forecasting Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02MAPE limitationsMAPE limitations is a practical concept within Forecasting Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03sMAPEsMAPE is a practical concept within Forecasting Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04MASEMASE is a practical concept within Forecasting Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Pinball lossPinball loss is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
06Backtesting metrics by horizonBacktesting metrics by horizon is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.