Geoffrey I. Webb

research

∙ 08/18/2023

CARLA: A Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection

We introduce a Self-supervised Contrastive Representation Learning Appro...

0 Zahra Zamanzadeh Darban, et al. ∙

research

∙ 08/02/2023

QUANT: A Minimalist Interval Method for Time Series Classification

We show that it is possible to achieve the same accuracy, on average, as...

0 Angus Dempster, et al. ∙

research

∙ 07/07/2023

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Time series are the primary data type used to record dynamic system meas...

0 Ming Jin, et al. ∙

research

∙ 05/26/2023

Improving Position Encoding of Transformers for Multivariate Time Series Classification

Transformers have demonstrated outstanding performance in many applicati...

0 Navid Mohammadi Foumani, et al. ∙

research

∙ 05/19/2023

An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set

The measurement of progress using benchmarks evaluations is ubiquitous i...

0 Ali Ismail-Fawaz, et al. ∙

research

∙ 04/12/2023

Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series

Time series classification (TSC) is a challenging task due to the divers...

0 Matthieu Herrmann, et al. ∙

research

∙ 02/06/2023

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Time Series Classification and Extrinsic Regression are important and ch...

0 Navid Mohammadi Foumani, et al. ∙

research

∙ 01/24/2023

Parameterizing the cost function of Dynamic Time Warping with application to time series classification

Dynamic Time Warping (DTW) is a popular time series distance measure tha...

0 Matthieu Herrmann, et al. ∙

research

∙ 11/16/2022

SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting

Threshold Autoregressive (TAR) models have been widely used by statistic...

0 Rakshitha Wathsadini Godahewa, et al. ∙

research

∙ 11/09/2022

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection has applications in a wide range of resear...

0 Zahra Zamanzadeh Darban, et al. ∙

research

∙ 03/25/2022

HYDRA: Competing convolutional kernels for fast and accurate time series classification

We demonstrate a simple connection between dictionary methods for time s...

0 Angus Dempster, et al. ∙

research

∙ 12/08/2021

Estimating Divergences in High Dimensions

The problem of estimating the divergence between 2 high dimensional dist...

0 Loong Kuan Lee, et al. ∙

research

∙ 11/26/2021

Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDT...

0 Matthieu Herrmann, et al. ∙

research

∙ 05/14/2021

Monash Time Series Forecasting Archive

Many businesses and industries nowadays rely on large quantities of time...

11 Rakshitha Wathsadini Godahewa, et al. ∙

research

∙ 02/20/2021

Elastic Similarity Measures for Multivariate Time Series Classification

Elastic similarity measures are a class of similarity measures specifica...

0 Ahmed Shifaz, et al. ∙

research

∙ 02/14/2021

Tight lower bounds for Dynamic Time Warping

Dynamic Time Warping (DTW) is a popular similarity measure for aligning ...

0 Geoffrey I. Webb, et al. ∙

research

∙ 02/10/2021

Early Abandoning and Pruning for Elastic Distances

Elastic distances are key tools for time series analysis. Straightforwar...

0 Matthieu Herrmann, et al. ∙

research

∙ 01/31/2021

MultiRocket: Effective summary statistics for convolutional outputs in time series classification

Rocket and MiniRocket, while two of the fastest methods for time series ...

0 Chang Wei Tan, et al. ∙

research

∙ 01/21/2021

Better Short than Greedy: Interpretable Models through Optimal Rule Boosting

Rule ensembles are designed to provide a useful trade-off between predic...

0 Mario Boley, et al. ∙

research

∙ 12/30/2020

Ensembles of Localised Models for Time Series Forecasting

With large quantities of data typically available nowadays, forecasting ...

0 Rakshitha Wathsadini Godahewa, et al. ∙

research

∙ 12/16/2020

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

Until recently, the most accurate methods for time series classification...

0 Angus Dempster, et al. ∙

research

∙ 11/12/2020

Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning

Supervised learning, characterized by both discriminative and generative...

0 Yuan Jin, et al. ∙

research

∙ 10/20/2020

An Eager Splitting Strategy for Online Decision Trees

We study the effectiveness of replacing the split strategy for the state...

0 Chaitanya Manapragada, et al. ∙

research

∙ 10/16/2020

Emergent and Unspecified Behaviors in Streaming Decision Trees

Hoeffding trees are the state-of-the-art methods in decision tree learni...

0 Chaitanya Manapragada, et al. ∙

research

∙ 10/16/2020

A Strong Baseline for Weekly Time Series Forecasting

Many businesses and industries require accurate forecasts for weekly tim...

0 Rakshitha Wathsadini Godahewa, et al. ∙

research

∙ 10/11/2020

Early Abandoning PrunedDTW and its application to similarity search

The Dynamic Time Warping ("DTW") distance is widely used in time series ...

0 Matthieu Herrmann, et al. ∙

research

∙ 06/23/2020

Time Series Regression

This paper introduces Time Series Regression (TSR): a little-studied tas...

0 Chang Wei Tan, et al. ∙

research

∙ 06/19/2020

Monash University, UEA, UCR Time Series Regression Archive

Time series research has gathered lots of interests in the last decade, ...

0 Chang Wei Tan, et al. ∙

research

∙ 05/25/2020

A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping

Land cover maps are a vital input variable to many types of environmenta...

38 Benjamin Lucas, et al. ∙

research

∙ 10/29/2019

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

Most methods for time series classification that attain state-of-the-art...

0 Angus Dempster, et al. ∙

research

∙ 10/10/2019

Time series classification for varying length series

Research into time series classification has tended to focus on the case...

0 Chang Wei Tan, et al. ∙

research

∙ 09/11/2019

InceptionTime: Finding AlexNet for Time Series Classification

Time series classification (TSC) is the area of machine learning interes...

135 Hassan Ismail Fawaz, et al. ∙

research

∙ 09/11/2019

DreamTime: Finding AlexNet for Time Series Classification

Time series classification (TSC) is the area of machine learning interes...

0 Hassan Ismail Fawaz, et al. ∙

research

∙ 06/25/2019

TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification

Time Series Classification (TSC) has seen enormous progress over the las...

0 Ahmed Shifaz, et al. ∙

research

∙ 11/26/2018

Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

New remote sensing sensors acquire now high spatial and spectral Satelli...

0 Charlotte Pelletier, et al. ∙

research

∙ 08/31/2018

Proximity Forest: An effective and scalable distance-based classifier for time series

Research into the classification of time series has made enormous progre...

0 Benjamin Lucas, et al. ∙

research

∙ 08/29/2018

Elastic bands across the path: A new framework and methods to lower bound DTW

There has been renewed recent interest in developing effective lower bou...

0 Chang Wei Tan, et al. ∙

research

∙ 08/26/2018

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing fuzzy neural networks (FNNs) are mostly developed under a shall...

0 Mahardhika Pratama, et al. ∙

research

∙ 08/07/2018

Instance-Dependent PU Learning by Bayesian Optimal Relabeling

When learning from positive and unlabelled data, it is a strong assumpti...

0 Fengxiang He, et al. ∙

research

∙ 01/29/2018

On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error

We propose two general and falsifiable hypotheses about expectations on ...

0 Nayyar A. Zaidi, et al. ∙

research

∙ 09/12/2017

Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining

We present theoretical analysis and a suite of tests and procedures for ...

0 Wilhelmiina Hämäläinen, et al. ∙

research

∙ 08/25/2017

Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes

This paper introduces a novel parameter estimation method for the probab...

0 Francois Petitjean, et al. ∙

research

∙ 11/12/2015

Characterizing Concept Drift

Most machine learning models are static, but the world is dynamic, and i...

0 Geoffrey I. Webb, et al. ∙

research

∙ 06/26/2015

Skopus: Exact discovery of the most interesting sequential patterns under Leverage

This paper presents a framework for exact discovery of the most interest...

0 Francois Petitjean, et al. ∙

Geoffrey I. Webb

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