1.6k citations · 4.7k across the 46 of their papers we have counts for
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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…