Module 3- part 2- ETS (Error, Trend, Seasonality) timeseries models скачать в хорошем качестве

Module 3- part 2- ETS (Error, Trend, Seasonality) timeseries models

Relevant playlists: Deep Forecasting Concepts, simply explained:    • Deep Forecasting codes and concepts (Simpl...   Machine Learning Codes and Concepts:    • Machine Learning Codes and Concepts (Simpl...   Deep Learning Concepts, simply explained:    • Deep Learning Codes and Concepts (Simply E...   Instructor: Pedram Jahangiry All of the slides and notebooks used in this series are available on my GitHub page, so you can follow along and experiment with the code on your own. https://github.com/PJalgotrader Lecture Timelines: 0:00 Exponential Smoothing methods, recap 5:54 State Space models for exponential smoothing methods 14:32 SES method estimation (MLE) 23:28 ETS model errors (additive vs multiplicative) 30:30 Maximul Likelihood Estimation (MLE), reminder 41:34 ETS model estimation 49:43 ETS(A, N, N) details 59:14 All the ETS models 1:02:29 ETS model Selection (AIC, AICc, BIC) 1:09:02 ETS forecasts + prediction intervals