A Hands-on Introduction to Time Series Classification and Regression

Research areas:

• Uncategorized

Year:

2024

Type of Publication:

In Proceedings

Keywords:

Time series, Machine learning, Classification, Extrinsic regression

Authors:

• Bagnall, Anthony
• Middlehurst, Matthew
• Forestier, Germain
• Ismail-Fawaz, Ali
• Guillaume, Antoine
• Guijo-Rubio, David
• Tan, Chang Wei
• Dempster, Angus
• Webb, Geoffrey I

Book title:

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages:

6410-6411

Abstract:

Time series classification and regression are rapidly evolving fields that find areas of application in all domains of machine learning and data science. This hands on tutorial will provide an accessible overview of the recent research in these fields, using code examples to introduce the process of implementing and evaluating an estimator. We will show how to easily reproduce published results and how to compare a new algorithm to state-of-the-art. Finally, we will work through real world examples from the field of Electroencephalogram (EEG) classification and regression. EEG machine learning tasks arise in medicine, brain-computer interface research and psychology. We use these problems to how to compare algorithms on problems from a single domain and how to deal with data with different characteristics, such as missing values, unequal length and high dimensionality. The latest advances in the fields of time series classification and regression are all available through the aeon toolkit, an open source, scikit-learn compatible framework for time series machine learning which we use to provide our code examples.