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Feature Engineering for Time Series Forecasting
Welcome
Introduction
Course curriculum
Course requirements (2:28)
Course material (1:54)
Download Jupyter notebooks
Download presentations
Download datasets
Setting up your computer
Time Series Forecasting - Overview
Time series forecasting (9:48)
Forecasting models (6:15)
Datasets, features and targets (6:50)
Forecasting framework (11:27)
Feature engineering overview (9:32)
Forecasting demo: data analysis (17:54)
Forecasting demo: feature engineering (19:00)
Forecasting demo: seasonality features (7:38)
Forecasting demo: training the forecaster (16:09)
Forecasting demo: forecasting multiple time series (5:04)
Backtesting or cross-validation (12:56)
Summary (10:12)
Challenges in feature engineering for forecasting
Challenges in feature engineering (4:22)
Machine learning workflow (4:10)
Feature engineering in tabular data (9:37)
Feature engineering in forecasting - considerations (14:42)
Forecasting one step ahead: demo (9:13)
Multistep forecasting
Direct multistep forecasting: demo (5:08)
Recursive multistep forecasting: demo (8:27)
Summary (3:09)
Time Series Decomposition
Components of a time series (7:10)
Additive and multiplicative models (7:13)
Log transform (5:27)
Moving average (13:59)
Moving averages in Pandas: demo (7:03)
Classical decomposition: trend (8:27)
Classical decomposition: seasonality (9:58)
Classical decomposition: demo (5:48)
LOWESS: Theory (11:26)
LOWESS: Practice (6:42)
LOWESS to extract trend: demo (12:29)
STL Overview (13:18)
STL theory part 1: LOWESS and cycle-subseries (8:53)
STL theory part 2: the inner loop (13:28)
STL theory part 3: the outer loop (4:41)
STL to compute seasonality and trend: demo (7:42)
Summary (14:09)
Missing Data Imputation
Imputation overview (5:58)
Forward and backward filling (3:03)
Forward and backward filling: demo (5:58)
Linear interpolation (4:42)
Linear interpolation: demo (5:51)
Spline interpolation (4:51)
Spline interpolation: demo (3:43)
Seasonal decomposition and interpolation (2:32)
Seasonal decomposition and interpolation: demo (6:42)
Summary (6:04)
Outliers
Outliers overview (9:38)
Outliers in time series (5:21)
Rolling statistics (8:27)
Rolling mean for outlier detection (8:31)
Rolling mean for outlier detection: demo (10:13)
Rolling median for outlier detection (7:11)
Rolling median for outlier detection: demo (9:16)
Residuals for outlier detection (8:39)
LOWESS for outlier detection (5:03)
LOWESS and residuals for outlier detection: demo (10:07)
STL for outlier detection (3:58)
STL and residuals for outlier detection: demo (10:02)
Summary (8:44)
Lag Features
Lag features overview (4:48)
More lectures coming soon (7:38)
Window Features
Window features overview (3:01)
More lectures coming soon (4:06)
Seasonality and trend features
Seasonality and trend features overview (7:42)
More lectures coming soon (7:21)
Date and Time Features
Date and time features (4:20)
Date features: Demo (17:18)
Time features: Demo (4:14)
Datetime features with Feature-engine (5:36)
Periodic or Cyclical Features (6:29)
Periodic Features: Demos (8:20)
Time since - Resency of events (3:01)
Time since: Demo (3:01)
Summary
Categorical Features
One hot encoding (3:14)
One hot encoding: demo (6:11)
Target encoding
Target encoding: demo
Rolling entropy
Rolling entropy: demo
Rolling majority
Summary
Automating feature creation
tsfresh: coming soon (4:43)
featuretools: coming soon (8:17)
Summary (4:34)
Assembling feature engineering pipelines
Putting it all together (6:43)
Regression pipeline (13:51)
Final section | Next steps
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