scholarly journals Probability Ridges and Distortion Flows: Visualizing Multivariate Time Series Using a Variational Bayesian Manifold Learning Method

2014 ◽  
pp. 55-64 ◽  
Author(s):  
Alessandra Tosi ◽  
Iván Olier ◽  
Alfredo Vellido
Keyword(s):  
Time Series ◽  
Manifold Learning ◽  
Multivariate Time Series ◽  
Learning Method ◽  
Variational Bayesian

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 Univariate and Multivariate Time Series Manifold Learning

2017 ◽  
Vol 133 ◽  
pp. 1-16 ◽  
Author(s):  
Colin O’Reilly ◽  
Klaus Moessner ◽  
Michele Nati
Keyword(s):  
Time Series ◽  
Manifold Learning ◽  
Multivariate Time Series
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