1.6k citations · 4.7k across the 46 of their papers we have counts for
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QUANT: A Minimalist Interval Method for Time Series Classification
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark d…
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan +2
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learnin…
Improving Position Encoding of Transformers for Multivariate Time Series Classification
Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb +1
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to…
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
Ali Ismail-Fawaz, Angus Dempster, Chang Wei Tan +8
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning. However, common approaches to analyzing and presenting the results…
Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series
Matthieu Herrmann, Chang Wei Tan, Mahsa Salehi +1
Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance,…
Parameterizing the cost function of Dynamic Time Warping with application to time series classification
Matthieu Herrmann, Chang Wei Tan, Geoffrey I. Webb
Dynamic Time Warping (DTW) is a popular time series distance measure that aligns the points in two series with one another. These alignments support warping of the time dimension t…