scholarly journals Bayesian Method for Causal Inference in Spatially-Correlated Multivariate Time Series

2019 ◽  
Vol 14 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Bo Ning ◽  
Subhashis Ghosal ◽  
Jewell Thomas
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Bayesian Method ◽  
Multivariate Time Series ◽  
Spatially Correlated

scholarly journals Spatially Correlated Time Series and Ecological Niche Analysis of Cutaneous Leishmaniasis in Afghanistan

2016 ◽  
Author(s):  
Oyelola A. Adegboye ◽  
Majeed Adegboye
Keyword(s):  
Time Series ◽  
Cutaneous Leishmaniasis ◽  
Spatial Data ◽  
Ecological Niche ◽  
Ecological Niche Modeling ◽  
Multivariate Time Series ◽  
Steady Increase ◽  
Niche Modelling ◽  
Spatially Correlated ◽  
Presence Data

Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to investigate the geographical suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows was observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-- January to March and September to December -- which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

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.

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