Interval Forecasting

Interval forecasting is a statistical and machine learning technique that predicts a range or interval of possible future values for a time series, rather than a single point estimate. It quantifies uncertainty by providing prediction intervals, such as confidence intervals or prediction intervals, which indicate the likely range where future observations will fall with a specified probability. This approach is widely used in fields like finance, supply chain management, and energy demand prediction to assess risk and make more informed decisions under uncertainty.

Also known as: Prediction Intervals, Confidence Intervals for Forecasting, Uncertainty Quantification in Time Series, Probabilistic Forecasting, Range Forecasting

Why learn Interval Forecasting?

Developers should learn interval forecasting when building applications that require robust predictions with uncertainty quantification, such as financial risk assessment, inventory optimization, or weather forecasting. It is particularly valuable in scenarios where decision-making depends on understanding the range of possible outcomes, helping to mitigate risks and improve planning accuracy. For example, in e-commerce, it can predict sales intervals to optimize stock levels and reduce shortages or overstock.

Learning Resources

• docs: Forecasting: Principles and Practice - Prediction Intervals
• tutorial: Scikit-learn: Prediction Intervals for Gradient Boosting
• course: Coursera: Practical Time Series Analysis
• video: YouTube: Introduction to Prediction Intervals in Time Series
• book: Book: 'Forecasting with Uncertainty' by Spyros Makridakis et al.