Get Started in Time Series Forecasting in Python | Full Course

Serious about mastering time series forecasting and ready to take the next step - enroll today in Applied Time Series Forecasting in Python and enjoy an 60% discount code: https://www.datasciencewithmarco.com/offers/zTAs2hi6?coupon_code=ATSFP60 Lifetime access to the course, modules are regularly updated, and your questions are answered by me directly! Course material: - 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 This video is the perfect starting point for beginners looking to forecast time series data. We use 100% Python code to cover the fundamental concepts of time series forecasting: - defining time series data - time series decomposition - forecasting with ARIMA - cross-validation in time series - using exogenous features - generating prediction intervals - evaluation metrics for forecasting models Chapters: - 0:00 Introduction - 1:20 Define time series - 5:38 Baseline models - 9:10 Baseline models (code) - 23:22 ARIMA - 31:01 ARIMA (code) - 38:21 Cross-validation - 40:38 Cross-validation (code) - 49:56 Forecasting with exogenous features - 54:28 Exogenous features (code) - 1:08:10 Prediction intervals - 1:09:32 Prediction intervals (code) - 1:14:31 Evaluation metrics - 1:18:58 Evaluation metrics (code) - 1:29:25 Next steps