An uncertainty and density based active semi-supervised learning scheme for positive unlabeled multivariate time series classification

2017 ◽  
Vol 124 ◽  
pp. 80-92 ◽  
Author(s):  
Guoliang He ◽  
Yifei Li ◽  
Wen Zhao
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Multivariate Time Series ◽  
Time Series Classification ◽  
Learning Scheme

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.

Time-Frequency Deep Metric Learning for Multivariate Time Series Classification

2021 ◽  
Author(s):  
Zhi Chen ◽  
Yongguo Liu ◽  
Jiajing Zhu ◽  
Yun Zhang ◽  
Rongjiang Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Time Series Classification ◽  
Time Frequency ◽  
Deep Metric Learning

Stacking for multivariate time series classification

2013 ◽  
Vol 18 (2) ◽  
pp. 297-312 ◽  
Author(s):  
Oscar J. Prieto ◽  
Carlos J. Alonso-González ◽  
Juan J. Rodríguez
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Time Series Classification
Sign in / Sign up

Export Citation Format

Share Document