Feature Engineering for Time Series Forecasting
Feature Engineering for Time Series Forecasting
Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers.
Created by Soledad Galli, Kishan Manani | 21 hours on-demand video course
Welcome to Feature Engineering for Time Series Forecasting, the most comprehensive course on feature engineering for forecasting available online. In this course, you will learn how to create and extract features from time series data for use in forecasting. Master the Art of Feature Engineering for Time Series Forecasting. In this course, you will learn multiple feature engineering methods to extract and create features from time-series data that are suitable for forecasting with off-the-shelf regression models like linear regression, random forests, and gradient boosted machines.
What you’ll learn
- How to forecast using traditional machine learning models
- How to convert time series into a table of predictive features
- How to impute missing data for time series forecasting
- How to detect and remove outliers in time series forecasting
- How to create features from past data through lags and windows
- How to build features that capture seasonality and trend
- How to encode categorical variables for time series forecasting
- How to highlight special events, like holidays or advertising campaings
- How to forecast multiple steps in the future
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Time Series Analysis, Forecasting, and Machine Learning in Python
