Contents:
1:06 - Introduction
2:30 - Plan
5:05 - Notebooks overview
6:03 - scikit-learn workflow
11:45 - 1 - Introduction to ML with Time series
12:20 - Time series data formats
14:30 - Time series learning tasks
18:25 - sktime objectives
21:15 - 2 - Time series Forecasting with sktime
21:53 - Univariate forecasting - basic workflow
31:55 - Why not just use scikit-learn?
36:50 - Univariate forecasting - advanced workflow
43:05 - Automation and model evaluation
46:32 - Tuning and pipelining
52:39 - Univariate forecasting with exogenous variables
56:31 - Multivariate forecasting
1:01:05 - Building your own forecaster
1:04:30 - 3 - Time series Classification
1:05:45 - Univariate classification
1:14:20 - Time series Regression
1:16:40 - Multivariate classification
1:18:06 - Roadmap and how to get involved
https://www.youtube.com/watch?v=ODspi8-uWgo&list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0&index=3&ab_channel=PyData
Contents: 1:06 - Introduction 2:30 - Plan 5:05 - Notebooks overview 6:03 - scikit-learn workflow 11:45 - 1 - Introduction to ML with Time series 12:20 - Time series data formats 14:30 - Time series learning tasks 18:25 - sktime objectives
21:15 - 2 - Time series Forecasting with sktime 21:53 - Univariate forecasting - basic workflow 31:55 - Why not just use scikit-learn? 36:50 - Univariate forecasting - advanced workflow 43:05 - Automation and model evaluation 46:32 - Tuning and pipelining 52:39 - Univariate forecasting with exogenous variables 56:31 - Multivariate forecasting 1:01:05 - Building your own forecaster
1:04:30 - 3 - Time series Classification 1:05:45 - Univariate classification 1:14:20 - Time series Regression 1:16:40 - Multivariate classification 1:18:06 - Roadmap and how to get involved