kNN ensembles with penalized DTW for multivariate time series imputation

Oehmcke, Stefan; Krämer, Oliver

doi:10.1109/ijcnn.2016.7727549

Cited by 42 publications

(28 citation statements)

References 19 publications

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“…Up to now, numerous successful researches have been devoted to complete missing data in multivariate time series imputation such as [10,11,[20][21][22][23][24][25][26][27][28]. Imputation techniques can be categorized in different perspectives: model-based or machine learningbased and clustering-based imputation techniques.…”

Section: Classical Multivariate Imputation Methodsmentioning

confidence: 99%

“…Considering imputation methods for multivariate time series, taking advantage of the correlations between variables is commonly applied to predict lacking data [6][7][8][9][10][11]. This means that relations permit using the values of 2 Applied Computational Intelligence and Soft Computing available features to estimate the missing values of other features.…”

Section: Introductionmentioning

confidence: 99%

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A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Phan

Bigand

Caillault

2018

Applied Computational Intelligence and Soft Computing

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The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windows and . We then find the most similar subsequence ( ) to the subsequence before this gap and the most similar one ( ) to the subsequence after the gap . To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window following and the one preceding . The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.

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Section: Classical Multivariate Imputation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Phan

Bigand

Caillault

2018

Applied Computational Intelligence and Soft Computing

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“…Researchers have used various alternative methods for imputation of missing data [22]. The missing data is in various researches is handled by three traditional reasons:…”

Section: Reasons For Using Imputation Methodsmentioning

confidence: 99%

Technical Review : Performance of Existing Imputation Methods for Missing Data in SVM Ensemble Creation

Ali¹,

Dacey²

2017

IJDKP

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“…In the litterature, many studies have been devoted to the imputation task of multivariate time series, such as [6]- [18] and fewer for univariate time series with missing data [5], [19]- [22]. Correlation-based method [23], and machine learning approach [24] are often used for multivariate series: missing data is filled by the value of its representative computed from others variables.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the authors did not mention to complete long missing subsequences. In [18] a weighted k-NN version is combined with DTW to compare multiple points in time simultaneously. DTWcost is used as distance metric instead of pointwise distance measurements.…”

Section: Introductionmentioning

confidence: 99%

Which DTW method applied to marine univariate time series imputation

Phan

Caillault

Lefebvre

et al. 2017

OCEANS 2017 - Aberdeen

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Abstract-Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imputation of marine univariate time series. In the first step, the missing data are completed on various mono-dimensional time series. To fill a missing sub-sequence (gap) in a time series, we first find the most similar sub-sequence to the sub-sequence before (resp. after) this gap according a Dynamic Time Warping (DTW)-cost. Then we complete the gap by the next (resp. previous) sub-sequence of the most similar one. Through experiments results on 5 different datasets we conclude that i) DTW gives the best results when considering the accuracy of imputation values and ii) Adaptive Feature Based DTW (AFBDTW) metric yields very similar shape of imputation values similar to the one of true values.

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kNN ensembles with penalized DTW for multivariate time series imputation

Cited by 42 publications

References 19 publications

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Technical Review : Performance of Existing Imputation Methods for Missing Data in SVM Ensemble Creation

Which DTW method applied to marine univariate time series imputation

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