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