Conformal Predictions for Time Series Probabilistic Forecasting

Sample eBook chapters (free): https://github.com/dataman-git/modern-time-series/blob/main/20240522beauty_TOC.pdf

eBook on Teachable.com: $22.50
https://drdataman.teachable.com/p/home

The print edition on Amazon.com: $65 https://a.co/d/25FVsMx

Real-world applications and planning require probabilistic forecasts rather than a point estimate. Probabilistic forecasts, also called prediction intervals or prediction uncertainty, can give planners a sense of uncertainty. However, the typical machine learning models such as linear regressions, random forecasts, or gradient boosting machines, are designed to produce mean estimation rather than a range of possible values. Developing from a point estimate to prediction intervals is what this book is interested in and the modern time series modeling techniques are concerned about. In the series of probabilistic forecasting, we introduced the Monte Carlo simulation techniques in “Monte Carlo Simulation for Time Series Probabilistic Forecasting” and the quantile regression technique in “Quantile Regression for Time Series Probabilistic Forecasting”. In this chapter, we will introduce another popular method — the Conformal Prediction (CP).