Indexing and classifying gigabytes of time series under time warping

Tan, Chang Wei; Webb, Geoffrey I.; Petitjean, François

doi:10.1137/1.9781611974973.32

Cited by 45 publications

(39 citation statements)

References 19 publications

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“…First, we assessed the scalability of TS-CHIEF w.r.t training set size. We used a Satellite Image Time Series (SITS) dataset [45] composed of 1 million time series of length 46, with 24 classes. We evaluated the accuracy and the total runtime as a function of the number of training time series, starting from a subsample of 58, and logarithmically increasing up to 131,879 (a sufficient quantity to clearly define the trend).…”

Section: Increasing Training Set Sizementioning

confidence: 99%

“…It is orders of magnitude faster than both FLAT-COTE and HIVE-COTE while attaining accuracy that ranks at least as well as them when assessed on the benchmark UCR archive, as illustrated in Figure 1. Figure 2 shows an experiment that demonstrates the scalability of TS-CHIEF using the Satellite Image Time Series (SITS) dataset [45]. It is 900x faster than HIVE-COTE for 1,500 time series (13 min versus 8 days).…”

Section: Introductionmentioning

confidence: 99%

“…While being scalable and efficient, our method TS-CHIEF ranks marginally better on accuracy than the current state-of-the-art method HIVE-COTE. 7UDLQLQJVHWVL]Hn [45]. Even for 1,500 time series, TS-CHIEF is more than 900 times faster than the current state of the art HIVE-COTE.…”

Section: Introductionmentioning

confidence: 99%

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TS-CHIEF: a scalable and accurate forest algorithm for time series classification

Shifaz

Pelletier

Petitjean

et al. 2020

Data Min Knowl Disc

Self Cite

154

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Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series are a specific data type for which the traditional attribute-value representation, used predominantly in machine learning, fails to provide a relevant representation. HIVE-COTE combines multiple types of classifiers: each extracting information about a specific aspect of a time series, be it in the time domain, frequency domain or summarization of intervals within the series. However, HIVE-COTE (and its predecessor, FLAT-COTE) is often infeasible to run on even modest amounts of data. For instance, training HIVE-COTE on a dataset with only 1,500 time series can require 8 days of CPU time. It has polynomial runtime w.r.t training set size, so this problem compounds as data quantity increases. We propose a novel TSC algorithm, TS-CHIEF, which is highly competitive to HIVE-COTE in accuracy, but requires only a fraction of the runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time series that research has developed in the last decade. It uses tree-structured classifiers to do so efficiently. We assess TS-CHIEF on 85 datasets of the UCR archive, where it achieves state-of-the-art accuracy with scalability and efficiency. 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.

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Section: Increasing Training Set Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TS-CHIEF: a scalable and accurate forest algorithm for time series classification

Shifaz

Pelletier

Petitjean

et al. 2020

Data Min Knowl Disc

Self Cite

154

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“…Time Series Classification (TSC) problems are encountered in various real world data mining tasks ranging from health care [1]- [3] and security [4], [5] to food safety [6], [7] and power consumption monitoring [8], [9]. As deep learning models have revolutionized many machine learning fields such as computer vision [10] and natural language processing [11], [12], researchers recently started to adopt these models for TSC tasks [13].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Fawaz

Forestier

Weber

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time.Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.• The definition and formalization of adversarial attacks for

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“…Dynamic Time Warping (DTW) lower bounds play a key role in speeding up many forms of time series analytics [6,10,11,17]. Several lower bounds have been proposed [6-8, 15, 19].…”

Section: Introductionmentioning

confidence: 99%

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

Tan¹,

Petitjean²,

Webb³

2019

Proceedings of the 2019 SIAM International Conference on Data Mining

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The Nearest Neighbour algorithm coupled with the Dynamic Time Warping similarity measure (NN-DTW) is at the core of state-of-the-art classification algorithms including Ensemble of Elastic Distances and Collection of Transformation-Based Ensemble. DTW's complexity makes NN-DTW highly computationally demanding. To combat this, lower bounds to DTW are used to minimize the number of times the expensive DTW need be computed during NN-DTW search. Effective lower bounds must balance 'time to calculate' vs 'tightness to DTW.' On the one hand, the tighter the bound the fewer the calls to the full DTW. On the other, calculating tighter bounds usually requires greater computation. Numerous lower bounds have been proposed. Different bounds provide different trade-offs between computational time and tightness. In this work, we present a new class of lower bounds that are tighter than the popular Keogh lower bound, while requiring similar computation time. Our new lower bounds take advantage of the DTW boundary condition, monotonicity and continuity constraints. In contrast to most existing bounds, they remain relatively tight even for large windows. A single parameter to these new lower bounds controls the speed-tightness trade-off. We demonstrate that these new lower bounds provide an exceptional balance between computation time and tightness for the NN-DTW time series classification task, resulting in greatly improved efficiency for NN-DTW lower bound search.

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Indexing and classifying gigabytes of time series under time warping

Cited by 45 publications

References 19 publications

TS-CHIEF: a scalable and accurate forest algorithm for time series classification

TS-CHIEF: a scalable and accurate forest algorithm for time series classification

Adversarial Attacks on Deep Neural Networks for Time Series Classification

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

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