Search Results for author: Geoffrey I. Webb

Found 33 papers, 25 papers with code

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

1 code implementation25 Mar 2022 Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb

We present HYDRA, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both ROCKET and conventional dictionary methods.

Time Series Time Series Classification

Estimating Divergences in High Dimensions

1 code implementation8 Dec 2021 Loong Kuan Lee, Nico Piatkowski, François Petitjean, Geoffrey I. Webb

To this end, we show empirically that estimating the Kullback-Leibler divergence using decomposable models from a maximum likelihood estimator outperforms existing methods for divergence estimation in situations where dimensionality is high and useful decomposable models can be learnt from the available data.

Monash Time Series Forecasting Archive

1 code implementation14 May 2021 Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, Pablo Montero-Manso

Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area.

Time Series Time Series Forecasting

Elastic Similarity Measures for Multivariate Time Series Classification

1 code implementation20 Feb 2021 Ahmed Shifaz, Charlotte Pelletier, Francois Petitjean, Geoffrey I. Webb

However, except for multivariate versions of the well known Dynamic Time Warping (DTW) there is a lack of work to generalise other similarity measures for multivariate cases.

Classification Dynamic Time Warping +4

Tight lower bounds for Dynamic Time Warping

1 code implementation14 Feb 2021 Geoffrey I. Webb, Francois Petitjean

Due to DTW's high computation time, lower bounds are often employed to screen poor matches.

Dynamic Time Warping Time Series

Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping

2 code implementations10 Feb 2021 Matthieu Herrmann, Geoffrey I. Webb

This threshold, provided by the similarity search process, also allows to early abandon the computation of a distance itself.

Dynamic Time Warping Time Series +2

MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification

1 code implementation31 Jan 2021 Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I. Webb

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 ensembling structure of many state-of-the-art methods.

General Classification Time Series +1

Ensembles of Localised Models for Time Series Forecasting

1 code implementation30 Dec 2020 Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb, Slawek Smyl, Christoph Bergmeir

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 outperforming traditional univariate forecasting models that work on isolated series.

Time Series Time Series Forecasting

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

2 code implementations16 Dec 2020 Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb

ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier.

General Classification Time Series +1

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

no code implementations12 Nov 2020 Yuan Jin, Wray Buntine, Francois Petitjean, Geoffrey I. Webb

For this task, we survey a wide range of techniques available for handling missing values, self-supervised training and pseudo-likelihood training, and adapt them to a suite of algorithms that are suitable for the task.

Self-Supervised Learning

A Strong Baseline for Weekly Time Series Forecasting

1 code implementation16 Oct 2020 Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso

In particular, our model can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset.

Time Series Time Series Forecasting

Early Abandoning PrunedDTW and its application to similarity search

1 code implementation11 Oct 2020 Matthieu Herrmann, Geoffrey I. Webb

We show that EAPrunedDTW significantly improves the computation time of similarity search in the UCR Suite, and renders lower bounds dispensable.

Dynamic Time Warping Time Series +1

Time Series Extrinsic Regression

1 code implementation23 Jun 2020 Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb

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; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label.

Time Series Time Series Classification +2

Monash University, UEA, UCR Time Series Extrinsic Regression Archive

2 code implementations19 Jun 2020 Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb

We refer to this problem as Time Series Extrinsic Regression (TSER), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series.

Time Series Time Series Classification +2

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

1 code implementation25 May 2020 Benjamin Lucas, Charlotte Pelletier, Daniel Schmidt, Geoffrey I. Webb, François Petitjean

In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.

Domain Adaptation Time Series

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

5 code implementations29 Oct 2019 Angus Dempster, François Petitjean, Geoffrey I. Webb

Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets.

Classification General Classification +2

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

2 code implementations25 Jun 2019 Ahmed Shifaz, Charlotte Pelletier, Francois Petitjean, Geoffrey I. Webb

We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.

General Classification Time Series +1

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

1 code implementation26 Nov 2018 Charlotte Pelletier, Geoffrey I. Webb, Francois Petitjean

The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification.

Classification General Classification +2

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

4 code implementations31 Aug 2018 Benjamin Lucas, Ahmed Shifaz, Charlotte Pelletier, Lachlan O'Neill, Nayyar Zaidi, Bart Goethals, Francois Petitjean, Geoffrey I. Webb

We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.

General Classification Time Series +1

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

1 code implementation29 Aug 2018 Chang Wei Tan, Francois Petitjean, Geoffrey I. Webb

One of the key time series classification algorithms, the nearest neighbor algorithm with DTW distance (NN-DTW) is very expensive to compute, due to the quadratic complexity of DTW.

Dynamic Time Warping General Classification +2

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

no code implementations26 Aug 2018 Mahardhika Pratama, Witold Pedrycz, Geoffrey I. Webb

DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer.

Continual Learning

Instance-Dependent PU Learning by Bayesian Optimal Relabeling

no code implementations7 Aug 2018 Fengxiang He, Tongliang Liu, Geoffrey I. Webb, DaCheng Tao

Specifically, by treating the unlabelled data as noisy negative examples, we could automatically label a group positive and negative examples whose labels are identical to the ones assigned by a Bayesian optimal classifier with a consistency guarantee.

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

1 code implementation29 Jan 2018 Nayyar A. Zaidi, Geoffrey I. Webb, Francois Petitjean, Germain Forestier

These hypotheses lead to the concept of the sweet path, a path through the 3-d space of alternative drift rates, forgetting rates and bias/variance profiles on which generalization error will be minimized, such that slow drift is coupled with low forgetting and low bias, while rapid drift is coupled with fast forgetting and low variance.

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

no code implementations12 Sep 2017 Wilhelmiina Hämäläinen, Geoffrey I. Webb

We present theoretical analysis and a suite of tests and procedures for addressing a broad class of redundant and misleading association rules we call \emph{specious rules}.

Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes

4 code implementations25 Aug 2017 Francois Petitjean, Wray Buntine, Geoffrey I. Webb, Nayyar Zaidi

The main result of this paper is to show that improved parameter estimation allows BNCs to outperform leading learning methods such as Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.

General Classification

Understanding Concept Drift

1 code implementation2 Apr 2017 Geoffrey I. Webb, Loong Kuan Lee, François Petitjean, Bart Goethals

Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning.

On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers

1 code implementation24 Jan 2017 Nayyar A. Zaidi, Yang Du, Geoffrey I. Webb

It is often motivated by the limitation of some learners to qualitative data.

Characterizing Concept Drift

no code implementations12 Nov 2015 Geoffrey I. Webb, Roy Hyde, Hong Cao, Hai Long Nguyen, Francois Petitjean

This supports the development of the first comprehensive set of formal definitions of types of concept drift.

Deep Broad Learning - Big Models for Big Data

no code implementations4 Sep 2015 Nayyar A. Zaidi, Geoffrey I. Webb, Mark J. Carman, Francois Petitjean

For some learning tasks there is power in learning models that are not only Deep but also Broad.

Skopus: Mining top-k sequential patterns under leverage

1 code implementation26 Jun 2015 Francois Petitjean, Tao Li, Nikolaj Tatti, Geoffrey I. Webb

It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest.

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