L61 Tan thesis-final (1)_Redacted.pdf (10.47 MB)

Time Series Classification at Scale

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thesis
posted on 02.04.2019 by CHANG WEI TAN
This thesis develops scalable algorithms and techniques to classify large amount of time series data. Nowadays, many real-world applications are generating huge amount of time series data. This wealth of data is required to create finer and more accurate classification models that allow us to learn from the data. Unfortunately, the state-of-the-art classification algorithms are impractical for large amount of time series data. Models that are accurate but slow are not good. Therefore, the ability to classify large amount of data quickly and accurately allows the state of the art to be more practical in many applications.

History

Campus location

Australia

Principal supervisor

Geoff Ian Webb

Additional supervisor 1

Francois Petitjean

Additional supervisor 2

Paul Reichl

Year of Award

2019

Department, School or Centre

Clayton School of IT

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Information Technology

Exports