Pattern recognition and classification for multivariate time series

Spiegel, Stephan; Gaebler, Julia; Lommatzsch, Andreas; Luca, Ernesto William De; Albayrak, Şahin

doi:10.1145/2003653.2003657

Cited by 53 publications

(47 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

Section: B Machine Learning Approaches For Classification Of Time Sementioning

confidence: 99%

“…Spiegel et al [4] worked on Pattern Recognition and Classification for Multivariate Time Series. Their approach starts by splitting a time series into segments, and then clustering the recognized segments into groups with similar contexts [4]. Lin et al introduce a novel approach of symbolic representation of time series, that is suitable for streaming algorithms [3].…”

Section: B Machine Learning Approaches For Classification Of Time Sementioning

confidence: 99%

See 1 more Smart Citation

UAV degradation identification for pilot notification using machine learning techniques

Manukyan

Olivares-Méndez

Bissyandé

et al. 2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

View full text Add to dashboard Cite

Abstract-Unmanned Aerial Vehicles are currently investigated as an important sub-domain of robotics, a fast growing and truly multidisciplinary research field. UAVs are increasingly deployed in real-world settings for missions in dangerous environments or in environments which are challenging to access. Combined with autonomous flying capabilities, many new possibilities, but also challenges, open up. To overcome the challenge of early identification of degradation, machine learning based on flight features is a promising direction. Existing approaches build classifiers that consider their features to be correlated. This prevents a fine-grained detection of degradation for the different hardware components. This work presents an approach where the data is considered uncorrelated and, using machine learning techniques, allows the precise identification of UAV's damages.

show abstract

Section: B Machine Learning Approaches For Classification Of Time Sementioning

confidence: 99%

Section: B Machine Learning Approaches For Classification Of Time Sementioning

confidence: 99%

UAV degradation identification for pilot notification using machine learning techniques

Manukyan

Olivares-Méndez

Bissyandé

et al. 2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

View full text Add to dashboard Cite

show abstract

“…Without loss of generality, the initial data can be interpreted as a time series, which is a model commonly used in statistics as well as signal processing (Spiegel et al, 2011). The data is therefore represented by a finite sequence of T equally-sized vectors, denoted by V = {v t }, where each element v t ∈ R D and the index t ∈ [1, T ].…”

Section: Eigenspace Updating For High-dimensional Systemsmentioning

confidence: 99%

An efficient eigenspace updating scheme for high-dimensional systems

Gangl¹,

Mongus²,

Žalik³

2014

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

Systems based on principal component analysis have developed from exploratory data analysis in the past to current data processing applications which encode and decode vectors of data using a changing projection space (eigenspace). Linear systems, which need to be solved to obtain a constantly updated eigenspace, have increased significantly in their dimensions during this evolution. The basic scheme used for updating the eigenspace, however, has remained basically the same: (re)computing the eigenspace whenever the error exceeds a predefined threshold. In this paper we propose a computationally efficient eigenspace updating scheme, which specifically supports high-dimensional systems from any domain. The key principle is a prior selection of the vectors used to update the eigenspace in combination with an optimized eigenspace computation. The presented theoretical analysis proves the superior reconstruction capability of the introduced scheme, and further provides an estimate of the achievable compression ratios.

show abstract

“…Time series classification has received a great deal of attention in the past, and it also brings some new challenges to the data mining and machine learning community [1]. A number of different approaches have been proposed for univariate time series classification [9][10][11], however, few papers are found about multivariate time series classification in the literature [6].…”

Section: Introductionmentioning

confidence: 99%

A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns

Zhou

Chan

2015

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Abstract. Multiple variables and high dimensions are two main challenges for classification of Multivariate Time Series (MTS) data. In order to overcome these challenges, feature extraction should be performed before performing classification. However, the existing feature extraction methods lose the important correlations among the variables while reducing high dimensions of MTS. Hence, in this paper, we propose a new feature extraction method combined with different classifiers to provide a general classification strategy for MTS data which can be applied for different area problems of MTS data. The proposed algorithm can handle data of high feature dimensions efficiently with unequal length and discover the relationship within the same and between different component univariate time series for MTS data. Hence, the proposed feature extraction method is application-independent and therefore does not depend on domain knowledge of relevant features or assumption about underling data models. We evaluate the algorithm on one synthetic dataset and two real-world datasets. The comparison experimental result shows that the proposed algorithm can achieve higher classification accuracy and F-measure value.

show abstract

Pattern recognition and classification for multivariate time series

Cited by 53 publications

References 9 publications

UAV degradation identification for pilot notification using machine learning techniques

UAV degradation identification for pilot notification using machine learning techniques

An efficient eigenspace updating scheme for high-dimensional systems

A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns

Contact Info

Product

Resources

About