Generalized random shapelet forests

Karlsson, Isak; Papapetrou, Panagiotis; Boström, Henrik

doi:10.1007/s10618-016-0473-y

Cited by 123 publications

(67 citation statements)

References 37 publications

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“…Generalized Random Shapelet Forest (gRSF) [21] extracts a shapelet from a randomly chosen time series and finds the distance between this time series and each other time series. The data is then split according to whether it is above or below a threshold distance to the representative shapelet.…”

Section: Decision Tree Approachesmentioning

confidence: 99%

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

Lucas

Shifaz

Pelletier

et al. 2019

Data Min Knowl Disc

126

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Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; which may explain why the primary research focus has been on creating algorithms that have high accuracy on relatively small datasets.This paper introduces Proximity Forest, an algorithm that learns accurate models from datasets with millions of time series, and classifies a time series in milliseconds. The models are ensembles of highly randomized Proximity Trees. Whereas conventional decision trees branch on attribute values (and usually perform poorly on time series), Proximity Trees branch on the proximity of time series to one exemplar time series or another; allowing us to leverage the decades of work into developing relevant measures for time series. Proximity Forest gains both efficiency and accuracy by stochastic selection of both exemplars and similarity measures.Our work is motivated by recent time series applications that provide orders of magnitude more time series than the UCR benchmarks. Our experiments demonstrate that Proximity Forest is highly competitive on the UCR archive: it ranks among the most accurate classifiers while being significantly faster. 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.

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Section: Decision Tree Approachesmentioning

confidence: 99%

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

Lucas

Shifaz

Pelletier

et al. 2019

Data Min Knowl Disc

126

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“…Moreover, various voting approaches are evaluated for providing the final classification label, demonstrating that one shapelet tree per dimension outperforms shapelets defined over multiple dimensions [5]. More recently, the generalized random shapelet forest has been proposed for univariate and multivariate time series classification, by expanding the idea of random shapelet trees and randomly selecting shapelet features per dimension [20]. While this approach can achieve competitive performance against existing classifiers in terms of classification accuracy, it is a black-box classifier with limited interpretability and explainability of the predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Several time series classification models have been proposed in the literature, including distance-based classifiers (see, e.g., Ding et al [11] for a thorough review), shapelet-based classifiers [42,42] along with optimizations for shapelet selection or generation [15][16][17]39], and ensemble-based classifiers [2]. Recently, the random shapelet forest classifier (RSF) [20] has been proposed for classifying univariate and multivariate time series. The main idea is to build a set of decision trees, where each feature corresponds to a shapelet.…”

Section: Introductionmentioning

confidence: 99%

Locally and globally explainable time series tweaking

et al. 2019

Self Cite

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Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on three instantiations of the problem using global and local transformations. In the former case, we investigate the k-nearest neighbor classifier and provide an algorithmic solution to the global time series tweaking problem. In the latter case, we investigate the random shapelet forest classifier and focus on two instantiations of the local time series tweaking problem, which we refer to as reversible and irreversible time series tweaking, and propose two algorithmic solutions for the two problems along with simple optimizations. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.

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“…Moreover, various voting approaches are evaluated for providing the final classification label, demonstrating that one shapelet tree per dimension outperforms shapelets defined over multiple dimensions [29]. More recently, the generalized random shapelet forest has been proposed for univariate and multivariate time series classification, by expanding the idea of random shapelet trees and randomly selecting shapelet features per dimension [9]. While this approach can achieve competitive performance against existing classifiers in terms of classification accuracy it is a black-box classifier with limited interpretability and explainability of the predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Any distance or similarity measure can be employed as a cost function. In this paper, we use the Euclidean distance, and consider two instantiations of Problem 1, where f is the random shapelet forest (RSF) classifier [9]. IV.…”

Section: Problem Formulationmentioning

confidence: 99%

Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations

Karlsson

Rebane

Papapetrou

et al. 2018

2018 IEEE International Conference on Data Mining (ICDM)

Self Cite

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Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking. The classifier under investigation is the random shapelet forest classifier. Moreover, we propose two algorithmic solutions for the two problems along with simple optimizations, as well as a baseline solution using the nearest neighbor classifier. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.

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Generalized random shapelet forests

Cited by 123 publications

References 37 publications

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

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

Locally and globally explainable time series tweaking

Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations

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