Modeling and forecasting time series of compositional data: A generalized Dirichlet power steady model

Mohamad Mehdi, Elise Epaillard, Nizar Bouguila, Jamal Bentahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper presents GDPSM a power steady model (PSM) based on generalized Dirichlet observations for modeling and predicting compositional time series. The model’s unobserved states evolve according to the generalized Dirichlet conjugate prior distributions. The observations’ distribution is transformed into a set of Beta distributions each of which is re-parametrized as a unidimensional Dirichlet in its exponential form. We demonstrate that dividing the modeling problem into multiple smaller problems leads to more accurate predictions. We evaluate this model with the web service selection application. Specifically, we analyze the proportions of the quality classes that are assigned to the web services interactions. Our model is compared with another PSM that assumes Dirichlet observations. The experiments show promising results in terms of precision errors and standardized residuals.

Original languageBritish English
Title of host publicationScalable Uncertainty Management - 9th International Conference, SUM 2015, Proceedings
EditorsAlex Dekhtyar, Christoph Beierle
PublisherSpringer Verlag
Pages170-185
Number of pages16
ISBN (Print)9783319235394
DOIs
StatePublished - 2015
Event9th International Conference on Scalable Uncertainty Management, SUM 2015 - Quebec City, Canada
Duration: 16 Sep 201518 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9310
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Scalable Uncertainty Management, SUM 2015
Country/TerritoryCanada
CityQuebec City
Period16/09/1518/09/15

Keywords

  • Generalized Dirichlet
  • State space models
  • Time series

Fingerprint

Dive into the research topics of 'Modeling and forecasting time series of compositional data: A generalized Dirichlet power steady model'. Together they form a unique fingerprint.

Cite this