Forecasting pipelines | Train in Data

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Forecasting with Machine Learning

Welcome

Introduction (8:13)
Course curriculum (5:33)
Course overview (6:05)
Course requirements (2:25)
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Course material

Course material (1:48)
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Download presentations
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Time series as regression

Time series overview (10:17)
Forecasting overview (7:42)
Forecasting as regression (10:24)
Feature engineering (9:46)
Feature engineering - demo (13:09)
Feature engineering with Feature-engine (6:08)
Benchmark models (10:43)
Benchmark models - demo (10:00)
Single step forecasting with ML - demo (6:41)
Forecasting pipelines (9:47)
Summary (5:34)
A word from your instructor (0:26)
Quiz
Additional resources

Multistep forecasting

Multistep forecasting (4:37)
Recursive forecasting (6:38)
Recursive forecasting with lags - demo (11:51)
Recursive forecasting with future known features - demo (10:39)
Recursive forecasting with window features - demo (10:33)
Exercise 1 - recursive forecasting
Direct forecasting (9:03)
Direct forecasting with skforecast (6:16)
Direct forecasting with sklearn (16:58)
Direct forecasting with future known features (8:57)
Direct forecasting with future known features - skforecast (9:06)
Exercise 2 - direct forecasting
Wrap up (4:24)
A word from your instructor (0:44)
Quiz
Additional reading resources
Extra Treat: Our Reading Suggestion 📕

Multiseries forecasting

Introduction to forecasting multiple time series (3:47)
Multiple time series (7:30)
Local and global forecasting (8:52)
Forecasting multiple independent time series (8:11)
Forecasting multiple independent time series - data: demo (6:49)
Forecasting multiple independent time series - local forecasting: demo (14:46)
Forecasting multiple independent time series - global forecasting: demo (12:38)
Forecasting multiple independent time series - weights: demo (8:37)
Exercise 1: forecasting multiple independent time series
Forecasting multiple dependent time series (7:50)
Forecasting multiple dependent time series - data: demo (4:37)
Forecasting multiple dependent time series - forecasting: demo (11:35)
Exercise 2: forecasting multiple dependent time series
Summary (4:23)
Quiz
Additional reading materials

Backtesting

Introduction to backtesting (4:00)
Backtesting basics (8:20)
Backtesting multiple time series (5:59)
Backtesting strategies (9:24)
How to choose a backtesting strategy (10:58)
Backtesting without refitting the model (5:06)
Backtesting without refitting - single time series: demo (12:13)
Backtesting without refitting - custom error metrics: demo (4:00)
Backtesting without refitting - multiple time series: demo (10:16)
Backtesting with refitting the model and expanding training window (3:32)
Backtesting with refitting and expanding training window - single time series: demo (5:56)
Backtesting with refitting and expanding training window - multiple time series: demo (4:08)
Backtesting with refitting the model and rolling training window (2:21)
Backtesting with refitting and rolling training window - single time series: demo (4:06)
Backtesting with refitting and rolling training window - multiple time series: demo (6:29)
Backtesting with intermittent refitting (2:40)
Backtesting with intermittent refitting - single time series: demo (4:31)
Backtesting with intermittent refitting - multiple time series: demo (3:52)
Backtesting with a gap (1:50)
Backtesting with gap - single time series: demo (5:30)
Backtesting with a gap - multiple time series: demo (6:25)
Exercise 1: backtesting with a single time series
Exercise 2: backtesting with multiple time series
Summary (7:17)
Quiz
Additional reading material

Error metrics

Introduction to error metrics (9:20)
Structure of error metrics (part 1): overview of error metrics (13:12)
Structure of error metrics (part 2): multiple time series (2:49)
Structure of error metrics (part 3): what factors to consider (5:55)
Scale-dependent error metrics (part 1): definition (6:00)
Scale-dependent error metrics (part 2): pros, cons, and guidance (12:04)
Percentage error metrics (part 1): definition, pros and cons (15:27)
Percentage error metrics (part 2): modifications (10:22)
Percentage error metrics (part 3): guidance (5:20)
Relative error metrics and measures: definition, pros, cons, and guidance (9:28)
Scaled error metrics: definition, pros, cons, and guidance (10:46)
Error metrics for multiple time series (8:45)
Measuring bias (5:00)
Summary (6:46)
Exercise 1: Error metrics
Quiz
Additional reading material

Final section | Next steps

Congratulations
Next steps

Forecasting pipelines

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