Dense Bag-of-Temporal-SIFT-Words for Time Series Classification

Bailly, Adeline; Malinowski, Simon; Tavenard, Romain; Guyet, Thomas; Chapel, Laëtitia

doi:10.1007/978-3-319-44412-3_2

Cited by 25 publications

(40 citation statements)

References 27 publications

(44 reference statements)

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“…In [4], a Support Vector Machine was used to classify feature vectors. However, our objective is to assess the utility of the SIFT features in relation to the BOSS features.…”

Section: Bag Of Temporal Sift Words (Botsw) Classifiermentioning

confidence: 99%

“…In Section 4 we described two approaches from Computer Vision that may improve dictionary classifiers: SIFT features [23], adapted for time series as described in [4] and Spatial Pyramids [16] that have not formerly been used in this context. We also described the Histogram Intersection as an alternative distance measure between histograms.…”

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confidence: 99%

“…Table 4: The mean accuracy of five variants of dictionary classifiers. BOSS [24], spatial pyramid BOSS with histogram intersection distance (SP-HI) and BOSS distance (SP-BD) and an adapted version of bag of temporal words [4] with histogram intersection distance (BOTSW-BD) and BOSS distance (BOTSW-HI)…”

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confidence: 99%

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On time series classification with dictionary-based classifiers

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Bagnall

Malinowski

et al. 2019

IDA

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A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the histograms. A recent evaluation of two of this type of algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation Symbols (BOSS) found a significant difference in accuracy between these seemingly similar algorithms. We investigate this phenomenon by deconstructing the classifiers and measuring the relative importance of the four key components between BOP and BOSS. We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex. We conclude that BOSS represents the state of the art for dictionary based TSC. Both BOP and BOSS can be classed as bag of words approaches. These are particularly popular in Computer Vision for tasks such as image classification. Converting approaches from vision requires careful engineering. We adapt three techniques used in Computer Vision for TSC: Scale Invariant Feature Transform; Spatial Pyramids; and Histrogram Intersection. We find that using Spatial Pyramids in conjunction with BOSS (SP) produces a significantly more accurate classifier. SP is significantly more accurate than standard benchmarks and the original BOSS algorithm. It is not significantly worse than the best shapelet based approach, and is only outperformed by HIVE-COTE, an ensemble that includes BOSS as a constituent module.

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“…In [4], a Support Vector Machine was used to classify feature vectors. However, our objective is to assess the utility of the SIFT features in relation to the BOSS features.…”

Section: Bag Of Temporal Sift Words (Botsw) Classifiermentioning

confidence: 99%

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confidence: 99%

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On time series classification with dictionary-based classifiers

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Bagnall

Malinowski

et al. 2019

IDA

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“…[19] build words using coefficients of local polynomial approximations and use their histogram to classify repetitive time series. Bailly et al [4] adapted SIFT image descriptors for 1D domain. A BoF approach using statistical features is applied to identify general motion primitives for modeling different human activities [57].…”

Section: Related Workmentioning

confidence: 99%

Bag of recurrence patterns representation for time-series classification

Hatami

Gavet

Debayle

2018

Pattern Anal Applic

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Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag of Features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of "feature words" of a data-learned dictionary. This paper proposes embedding the Recurrence Plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC. While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them. Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treats TSC task as a texture recognition problem. Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed Bag of Recurrence patterns (BoR), compared not only to the existing BoF models, but also to the state-of-the art algorithms.

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“…Every time series is finally represented by a histogram of word occurrences that then feeds a classifier. Many feature-based approaches for time series classification can be found in the literature [2,3,4,19,25,26,27] and they mostly differ in the features they use.…”

Section: Introductionmentioning

confidence: 99%

Efficient Temporal Kernels Between Feature Sets for Time Series Classification

Tavenard

Malinowski

Chapel

et al. 2017

Machine Learning and Knowledge Discovery in Databases

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In the time-series classification context, the majority of the most accurate core methods are based on the Bag-of-Words framework, in which sets of local features are first extracted from time series. A dictionary of words is then learned and each time series is finally represented by a histogram of word occurrences. This representation induces a loss of information due to the quantization of features into words as all the time series are represented using the same fixed dictionary. In order to overcome this issue, we introduce in this paper a kernel operating directly on sets of features. Then, we extend it to a time-compliant kernel that allows one to take into account the temporal information. We apply this kernel in the time series classification context. Proposed kernel has a quadratic complexity with the size of input feature sets, which is problematic when dealing with long time series. However, we show that kernel approximation techniques can be used to define a good trade-off between accuracy and complexity. We experimentally demonstrate that the proposed kernel can significantly improve the performance of time series classification algorithms based on Bag-of-Words.

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Dense Bag-of-Temporal-SIFT-Words for Time Series Classification

Cited by 25 publications

References 27 publications

On time series classification with dictionary-based classifiers

On time series classification with dictionary-based classifiers

Bag of recurrence patterns representation for time-series classification

Efficient Temporal Kernels Between Feature Sets for Time Series Classification

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