Boost Forecast Accuracy: Implement Winkler Score for Prediction Intervals in Python

Learn how to evaluate prediction intervals like a data scientist using the Winkler score in Python! This beginner-friendly tutorial explains why prediction interval quality matters for emergency department demand forecasts, stock forecasts, and time series models. Discover how to: Calculate Winkler scores that balance interval width and accuracy penalties Compare ARIMA models vs seasonal naïve forecasts using emergency department data Implement custom scoring functions with NumPy for efficient vectorized operations Interpret results using pmdarima and visualize prediction interval performance Includes free Python code on GitHub for: Calculating multi-step Winkler scores Generating model comparison tables Creating prediction interval visualizations Like 👍 if you find it helpful, and subscribe for more practical forecasting and data science content! 💻    / @theopensciencenerd   👏 Massive thanks to goes to Prof Rob Hyndman and Prof George Athanasopoulos for their inspiring online book that taught me about the Winkler Score! 🌍 LINKS: ------------------------------------------------- 🔗 Github: https://github.com/TheOpenScienceNerd... 🔗 Forecasting Principles and Practice book: https://otexts.com/fpp3/ ☕ Buy me a coffee If you want to support the channel, you can buy me a coffee here: ☕ https://buymeacoffee.com/theopenscien... ⏳ Timestamps ⏳ ------------------------------------------------- 0:00 - Tutorial overview 0:53 - What is a winkler score? 2:14 - Calculation 4:05 - Python code setup 4:54 - Emergency department forecasting application 18:20 - Next steps #TimeSeries #PredictionIntervals #pythonbeginner #ARIMA #Forecasting #MachineLearning #DataScience #ModelEvaluation #PythonTutorial #DataAnalysis #ForecastAccuracy