A Reverse Nearest Neighbor Based Active Semi-supervised Learning Method for Multivariate Time Series Classification

2016 ◽  
pp. 272-286 ◽  
Author(s):  
Yifei Li ◽  
Guoliang He ◽  
Xuewen Xia ◽  
Yuanxiang Li
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Nearest Neighbor ◽  
Multivariate Time Series ◽  
Learning Method ◽  
Time Series Classification ◽  
Reverse Nearest Neighbor

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.

scholarly journals Causal Inference in Time Series via Supervised Learning

2018 ◽  
Author(s):  
Yoichi Chikahara ◽  
Akinori Fujino
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Causal Inference ◽  
Supervised Learning ◽  
Regression Models ◽  
Multivariate Time Series ◽  
Feature Representation ◽  
Learning Method ◽  
Conditional Distributions ◽  
Learning Framework

Causal inference in time series is an important problem in many fields. Traditional methods use regression models for this problem. The inference accuracies of these methods depend greatly on whether or not the model can be well fitted to the data, and therefore we are required to select an appropriate regression model, which is difficult in practice. This paper proposes a supervised learning framework that utilizes a classifier instead of regression models. We present a feature representation that employs the distance between the conditional distributions given past variable values and show experimentally that the feature representation provides sufficiently different feature vectors for time series with different causal relationships. Furthermore, we extend our framework to multivariate time series and present experimental results where our method outperformed the model-based methods and the supervised learning method for i.i.d. data.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

A deep multi-task representation learning method for time series classification and retrieval

2021 ◽  
Vol 555 ◽  
pp. 17-32
Author(s):  
Ling Chen ◽  
Donghui Chen ◽  
Fan Yang ◽  
Jianling Sun
Keyword(s):  
Time Series ◽  
Representation Learning ◽  
Learning Method ◽  
Time Series Classification ◽  
Task Representation

scholarly journals A Novel Channel and Temporal-Wise Attention in Convolutional Networks for Multivariate Time Series Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 212247-212257
Author(s):  
Xu Cheng ◽  
Peihua Han ◽  
Guoyuan Li ◽  
Shengyong Chen ◽  
Houxiang Zhang
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Time Series Classification ◽  
Convolutional Networks

scholarly journals Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

2021 ◽  
Author(s):  
Fatemehalsadat Madaeni ◽  
Karem Chokmani ◽  
Rachid Lhissou ◽  
Saeid Homayuni ◽  
Yves Gauthier ◽  
...  
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Weather Forecasting ◽  
Multivariate Time Series ◽  
Water Levels ◽  
Time Series Classification ◽  
Ice Jams ◽  
Ice Jam ◽  
Learning Techniques ◽  
Long Short Term Memory

Abstract. In cold regions, ice-jam events result in severe flooding due to a rapid rise in water levels upstream of the jam. These floods threaten human safety and damage properties and infrastructures as the floods resulting from ice-jams are sudden. Hence, the ice-jam prediction tools can give an early warning to increase response time and minimize the possible corresponding damages. However, the ice-jam prediction has always been a challenging problem as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. The ice-jam prediction problem can be considered as a binary multivariate time-series classification. Deep learning techniques have been successfully applied for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied CNN, LSTM, and combined CN-LSTM networks for ice-jam prediction for all the rivers in Quebec. The results show that the CN-LSTM model yields the best results in the validation and generalization with F1 scores of 0.82 and 0.91, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of them further improves classification.

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