Hierarchical Bayesian Fuzzy Clustering Approach for High Dimensional Linear Time-Series

Pacifico, Antonio (2020): Hierarchical Bayesian Fuzzy Clustering Approach for High Dimensional Linear Time-Series. Forthcoming in: NA , Vol. NA, No. Fuzzy Clustering Analysis : pp. 1-30.

Abstract

This paper develops a computational approach to improve fuzzy clustering and forecasting performance when dealing with endogeneity issues and misspecified dynamics in high dimensional dynamic data. Hierarchical Bayesian methods are used to structure linear time variations, reduce dimensionality, and compute a distance function capturing the most probable set of clusters among univariate and multivariate time-series. Nonlinearities involved in the procedure look like permanent shifts and are replaced by coefficient changes. Monte Carlo implementations are also addressed to compute exact posterior probabilities for each cluster chosen and then minimize the increasing probability of outliers plaguing traditional clustering time-series techniques. An empirical example highlights the strengths and limitations of the estimating procedure. Discussions with related works are also displayed.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARIMA Models; Forecasting; Distance Measures; Bayesian Model Averaging; Monte Carlo Algorithms; Dynamic Data.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis

References

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