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Bayesian structural time series

Bayesian Structural Time Series (BSTS) model is a machine learning technique used for feature selection, time series forecasting, nowcasting… 
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Related topics

Related topics

10 relations
Bayesian inference using Gibbs samplingCorrelation does not imply causationDifference in differencesEnsemble learning

Broader (1)

Machine learning

Papers overview

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2019
2019
Earthquake Damage Report Interactive Dashboard Using Bayesian Structural Time Series and Value-Suppressing Uncertainty Palettes
Earthquake Damage Report Interactive Dashboard addresses Mini Challenge 1 of VAST Challenge 2019. The project uses interactive… 
2018
2018
Forecasting Japan’s spot LNG prices using Bayesian Structural Time Series
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2017
2017
TV Impact on Online Searches
We study the impact of TV advertising on viewers’ online search behaviors. In particular, we develop methods to estimate how many… 
2015
2015
On the predictive performance of Bayesian structural time series-models
Diese Magisterarbeit untersucht die Vorhersagegenauigkeit von Bayesian Structural Time Series (BSTS) Modellen in empirischen… 
2015
2015
On the Predictive Performance of Bayesian Structural Time Series-Models : An Empirical Comparison of Nowcasting and Forecasting Applications
This thesis analyses the predictive accuracy of Bayesian Structural Time Series (BSTS) models in empirical nowcasting and… 
2015
2015
Business and Financial Cycles: an estimation of cycles’ length focusing on Macroprudential Policy
Business and financial cycles are important to Monetary and Macroprudential Policies. The Countercyclical Capital Buffer (CCB… 
2007
2007
The Dynamics of Reciprocity , Accountability , and Credibility 1
Do public opinion dynamics play an important role in understanding conflict trajectories between democratic governments and other… 
2006
2006
Reciprocity, Accountability and Credibility in International Relations
Do public opinion dynamics play an important role in understanding conflict dynamics between democracies and their international… 
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