LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization

Big Data ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 391-411
Author(s):  
Akihiro Yamaguchi ◽  
Shigeru Maya ◽  
Kohei Maruchi ◽  
Ken Ueno
Keyword(s):  
Time Series ◽  
Partial Auc ◽  
Learning Time ◽  
Auc Maximization

Learning Time Series by Neural Networks

ICANN ’94 ◽  
1994 ◽  
pp. 529-532 ◽  
Author(s):  
D. W. Allen ◽  
J. G. Taylor
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Learning Time

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

1998 ◽  
Vol 06 (02) ◽  
pp. 107-116 ◽  
Author(s):  
Sepp Hochreiter
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Time Lag ◽  
Neural Nets ◽  
Alternative Methods ◽  
Reasonable Time ◽  
Practical Applications ◽  
Learning Time ◽  
Long Time ◽  
Gradient Based

Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning methods take too much time. The extremely increased learning time arises because the error vanishes as it gets propagated back. In this article the de-caying error flow is theoretically analyzed. Then methods trying to overcome vanishing gradients are briefly discussed. Finally, experiments comparing conventional algorithms and alternative methods are presented. With advanced methods long time lag problems can be solved in reasonable time.

IoT Data Acquisition Node For Deep Learning Time Series Prediction

2021 ◽  
Author(s):  
Li Xinyun ◽  
Liu Huidan ◽  
Yin Hang ◽  
Cao Zilan ◽  
Chen Bangdi ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Acquisition ◽  
Time Series Prediction ◽  
Learning Time

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

2020 ◽  
Vol 127 ◽  
pp. 104666 ◽  
Author(s):  
Santiago Belda ◽  
Luca Pipia ◽  
Pablo Morcillo-Pallarés ◽  
Juan Pablo Rivera-Caicedo ◽  
Eatidal Amin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Vegetation Phenology ◽  
Gap Filling ◽  
Learning Time
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