scholarly journals IMPROVING TIME SERIES CLASSIFICATION ACCURACY: COMBINING GLOBAL AND LOCAL INFORMATION IN THE SIMILARITY CRITERION

2014 ◽  
Vol 44 (3) ◽  
pp. 225-229
Author(s):  
X. HE ◽  
C. SHAO ◽  
Y. XIONG
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Similarity Criterion ◽  
Haar Wavelet ◽  
Local Information ◽  
Time Series Classification ◽  
Shape Information ◽  
Haar Wavelet Transform ◽  
Invariant Distance ◽  
Global And Local

Given the widespread use of time series classification in many domains, how to improve the accuracy of classification has attracted considerable focus. In this paper, a new similarity measure (SIMscl) based on the global and local information has been proposed for improving the precision rate of one nearest neighbor (1NN) classifier. Specifically, the global information records the intrinsic properties of time series, and is reflected by two indicators: the shape information and the complexity; the local information pays attention to the exact match of value, and is realized by LB_keogh. Simultaneously, a method based on multi-scale discrete haar wavelet transform, key point extraction, and symbolization has been put forward to extract the shape information. To test the efficacy of the proposed shape similarity SIMshape and hybrid similarity SIMscl, the experiments are conducted on two data sets: star light curve and beef. Experimental evaluations show that SIMshape can deal with some time series misclassified by Euclidean Distance (ED), LB_keogh, and Complexity Invariant Distance (CID), and SIMscl has higher precision than ED, LB_keogh, and CID in time series 1NN classification.

scholarly journals WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 413 ◽  
Author(s):  
Chris Lytridis ◽  
Anna Lekova ◽  
Christos Bazinas ◽  
Michail Manios ◽  
Vassilis G. Kaburlasos
Keyword(s):  
Time Series ◽  
Ad Hoc ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Human Robot Interaction ◽  
Time Series Classification ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Knn Classifier ◽  
Benchmark Datasets

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

scholarly journals Data Augmentation with Suboptimal Warping for Time-Series Classification

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 98 ◽  
Author(s):  
Krzysztof Kamycki ◽  
Tomasz Kapuscinski ◽  
Mariusz Oszust
Keyword(s):  
Time Series ◽  
Data Augmentation ◽  
Nearest Neighbor ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Classification Performance ◽  
Training Dataset ◽  
Time Series Classification ◽  
Extensive Evaluation ◽  
The Impact

In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.

On the stopping criteria for k -Nearest Neighbor in positive unlabeled time series classification problems

2016 ◽  
Vol 328 ◽  
pp. 42-59 ◽  
Author(s):  
Mabel González ◽  
Christoph Bergmeir ◽  
Isaac Triguero ◽  
Yanet Rodríguez ◽  
José M Benítez
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Time Series Classification ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Stopping Criteria
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