A Hands-on Introduction to Time Series Classification and Regression

Áreas de investigación:

• Sin categoría

Año:

2024

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Time series, Machine learning, Classification, Extrinsic regression

Autores:

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

Título del libro:

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

Páginas:

6410-6411

BibTex:

@conference{bagnall2024hands, author = "Anthony Bagnall and Matthew Middlehurst and Germain Forestier and Ali Ismail-Fawaz and Antoine Guillaume and David Guijo-Rubio and Chang Wei Tan and Angus Dempster and Geoffrey I Webb", 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.", booktitle = "Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining", keywords = "Time series, Machine learning, Classification, Extrinsic regression", pages = "6410--6411", title = "{A} {H}ands-on {I}ntroduction to {T}ime {S}eries {C}lassification and {R}egression", url = "dl.acm.org/doi/abs/10.1145/3637528.3671443", year = "2024", }

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.