Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.
Extract features from time series to forecast with regression models.
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Kishan is a machine learning and data science lead, course instructor, and open source software contributor. He has contributed to well known Python packages including Statsmodels, Feature-engine, and Prophet. He presents at data science conferences including ODSC and PyData. Kishan attained a PhD in Physics from Imperial College London in applied large scale time-series analysis and modelling of cardiac arrhythmias; during this time he taught and supervised undergraduates and master's students.
Sole is a lead data scientist, instructor and developer of open source software. She created and maintains the Python library Feature-engine, which allows us to impute data, encode categorical variables, transform, create and select features. Sole is also the author of the "Python Feature engineering Cookbook" by Packt editorial.
Welcome to the most comprehensive course on Feature Engineering for Time Series 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 create features from time series data that are suitable for forecasting with off-the-shelf regression models like linear regression, tree-based models, and even neural networks.
The Challenges of Feature Engineering in Time Series Forecasting
Forecasting is the process of making predictions about the future based on past data. In the most traditional scenario, we have a time series and want to predict its future values. There are some challenges in creating forecasting features:
We can forecast future values of the time series using off-the-shelf regression models like linear regression, tree-based models, support vector machines, and more. However, these models require tabular data as input. For forecasting we don’t start with a table of features and a target variable, but instead a set of time series, perhaps just one. We need to transform the time series into tabular data with a target variable and a set of features that can be used by supervised learning models. Therefore, the main challenge is about creating a well-designed target variable and specially designed features that allow us to predict the future value of a time series.
