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A New Granular Approach for Multivariate Forecasting

  • Petrônio Cândido de Lima e SilvaEmail author
  • Carlos Alberto Severiano Jr.
  • Marcos Antônio Alves
  • Miri Weiss Cohen
  • Frederico Gadelha Guimarães
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1068)

Abstract

The research for computationally cheaper, scalable and explainable machine learning methods for time series analysis and forecasting has grown in recent years. One of these developments is the Fuzzy Time Series (FTS), simple and fast methods to create readable and accurate forecasting models. However, as the number of variables increase the complexity of these models becomes impractical. This work proposes the \(\mathcal {FIG}\)-FTS, a new approach to enable multivariate time series to be tackled as univariate FTS methods using composite fuzzy sets to represent each Fuzzy Information Granule (FIG). \(\mathcal {FIG}\)-FTS is flexible and highly adaptable, allowing the creation of weighted high order forecasting models capable to perform multivariate forecasting for many steps ahead. The proposed method was tested with Lorentz Attractor chaotic time series and the GEFCom 2012 electric load forecasting contest data, considering different forecasting horizons. The results showed that the Mean Average Percentual Error of the models was at about 2% and 4% for one step ahead, and for a prediction horizon of 48 h, the MAPE is at about 10%.

Keywords

Fuzzy Time Series Fuzzy Information Granule Multivariate time series 

Notes

Acknowledgements

This work has been supported by CAPES, CNPq and FAPEMIG funding agencies in Brazil, and the PBQS program of the IFNMG - Campus Januária institution.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Instituto Federal do Norte de Minas Gerais - Campus JanuáriaJanuáriaBrazil
  2. 2.Instituto Federal de Minas Gerais - Campus SabaráSobradinhoBrazil
  3. 3.Machine Intelligence and Data Science Lab (MINDS UFMG)Belo HorizonteBrazil
  4. 4.Department of Electrical EngineeringUniversidade Federal de Minas Gerais, UFMGBelo HorizonteBrazil
  5. 5.Department of Software EngineeringBraude College of EngineeringKarmielIsrael

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