1.6k citations · 4.7k across the 46 of their papers we have counts for
38 papers · 1 filter
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…
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…
HYDRA: Competing convolutional kernels for fast and accurate time series classification
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods ba…
Computing Divergences between Discrete Decomposable Models
Loong Kuan Lee, Nico Piatkowski, François Petitjean +1
There are many applications that benefit from computing the exact divergence between 2 discrete probability measures, including machine learning. Unfortunately, in the absence of a…
Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping
Matthieu Herrmann, Geoffrey I. Webb
Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDTW) variants, are time series distances with a wide range of applications. They minimize the cost of non-line…
Monash Time Series Forecasting Archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +2
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are t…
Tight lower bounds for Dynamic Time Warping
Geoffrey I. Webb, Francois Petitjean
Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW's high computation time, lower bounds are often employed to screen poo…
Elastic Similarity and Distance Measures for Multivariate Time Series
Ahmed Shifaz, Charlotte Pelletier, Francois Petitjean +1
This paper contributes multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. Elastic similarity and distance measure…
Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping
Matthieu Herrmann, Geoffrey I. Webb
Nearest neighbor search under elastic distances is a key tool for time series analysis, supporting many applications. However, straightforward implementations of distances require…
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification
Chang Wei Tan, Angus Dempster, Christoph Bergmeir +1
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art performance with a tiny fraction of the time and without the complex ensemb…
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
Mario Boley, Simon Teshuva, Pierre Le Bodic +1
Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability. However, the myopic and random search components of current rule e…
Ensembles of Localised Models for Time Series Forecasting
Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb +2
With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly…
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
Until recently, the most accurate methods for time series classification were limited by high computational complexity. ROCKET achieves state-of-the-art accuracy with a fraction of…
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning
Yuan Jin, Wray Buntine, Francois Petitjean +1
Supervised learning, characterized by both discriminative and generative learning, seeks to predict the values of single (or sometimes multiple) predefined target attributes based…
Emergent and Unspecified Behaviors in Streaming Decision Trees
Chaitanya Manapragada, Geoffrey I Webb, Mahsa Salehi +1
Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams. These very fast decision trees are used in many real applications where data i…
Early Abandoning PrunedDTW and its application to similarity search
Matthieu Herrmann, Geoffrey I. Webb
The Dynamic Time Warping ("DTW") distance is widely used in time series analysis, be it for classification, clustering or similarity search. However, its quadratic time complexity…
An Eager Splitting Strategy for Online Decision Trees
Chaitanya Manapragada, Heitor M Gomes, Mahsa Salehi +2
Decision tree ensembles are widely used in practice. In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online t…
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +1
Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, re…
Time Series Extrinsic Regression
Chang Wei Tan, Christoph Bergmeir, Francois Petitjean +1
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable;…
Monash University, UEA, UCR Time Series Extrinsic Regression Archive
Chang Wei Tan, Christoph Bergmeir, Francois Petitjean +1
Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has great…
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
Benjamin Lucas, Charlotte Pelletier, Daniel Schmidt +2
Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these te…