This course is an introduction to time series forecasting with Python. It's a perfect starting point for beginners looking to forecast time series data. You will learn the fundamental concepts of time series forecasting.
✏️ Course developed by @datasciencewithmarco
Solutions notebook: https://github.com/marcopeix/youtube_tutorials/blob/main/YT_03_forecasting_stats.ipynb
Dataset: https://github.com/marcopeix/youtube_tutorials/tree/main/data
Learn even more with this paid course: Applied Time Series Forecasting in Python
https://www.datasciencewithmarco.com/offers/zTAs2hi6/checkout?coupon_code=ATSFP60
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp
⭐️ Chapters ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:02:12) Define time series
⌨️ (0:06:30) Baseline models
⌨️ (0:10:02) Baseline models (code)
⌨️ (0:24:14) ARIMA
⌨️ (0:31:53) ARIMA (code)
⌨️ (0:39:13) Cross-validation
⌨️ (0:41:30) Cross-validation (code)
⌨️ (0:50:48) Forecasting with exogenous features
⌨️ (0:55:20) Exogenous features (code)
⌨️ (1:09:02) Prediction intervals
⌨️ (1:10:24) Prediction intervals (code)
⌨️ (1:15:23) Evaluation metrics
⌨️ (1:19:50) Evaluation metrics (code)
⌨️ (1:30:17) Next steps
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