You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 25 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.

1 code implementation • 8 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.

no code implementations • 26 Nov 2021 • Matthieu Herrmann, Geoffrey I. Webb

CDTW and WDTW have been introduced because DTW is too permissive in its alignments.

1 code implementation • 14 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.

1 code implementation • 20 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.

1 code implementation • 14 Feb 2021 • Geoffrey I. Webb, Francois Petitjean

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

2 code implementations • 10 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.

1 code implementation • 31 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.

1 code implementation • 30 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.

2 code implementations • 16 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.

no code implementations • 12 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.

1 code implementation • 16 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.

1 code implementation • 11 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.

1 code implementation • 23 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.

2 code implementations • 19 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.

1 code implementation • 25 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.

5 code implementations • 29 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.

no code implementations • 10 Oct 2019 • Chang Wei Tan, Francois Petitjean, Eamonn Keogh, Geoffrey I. Webb

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

6 code implementations • 11 Sep 2019 • Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean

TSC is the area of machine learning tasked with the categorization (or labelling) of time series.

2 code implementations • 25 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.

1 code implementation • 26 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.

4 code implementations • 31 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.

1 code implementation • 29 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.

no code implementations • 26 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.

no code implementations • 7 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.

1 code implementation • 29 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.

no code implementations • 12 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}.

4 code implementations • 25 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.

1 code implementation • 2 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.

1 code implementation • 24 Jan 2017 • Nayyar A. Zaidi, Yang Du, Geoffrey I. Webb

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

no code implementations • 12 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.

no code implementations • 4 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.

1 code implementation • 26 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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.