Abstract

The purpose of this study is to propose a hybrid model by combining statistical methods, namely Time Series Regression (TSR), Multivariate Generalized Space-Time Autoregressive (MGSTAR) as a space-time model, and Machine Learning (ML) to forecast multivariate Spatio-temporal data simultaneously. The linear model, namely TSR is used to capture trends and double seasonal patterns. MGSTAR is a model for capturing dependencies between locations. Meanwhile, capturing nonlinear patterns used the ML model. In this study, three types of ML model is used, i.e., Deep Learning Neural Network (DLNN), Feed Forward Neural Network (FFNN), and Long Short-Term Memory (LSTM). We apply this proposed method to simulated data. Based on the Root Mean Square Error (RMSE) value, the proposed hybrid methods, namely TSR-MGSTAR-DLNN, TSR-MGSTAR-FFNN, and TSR-MGSTAR-LSTM, outperform other models such as TSR, MGSTAR, MGSTAR.-DLNN, MGSTAR-FFNN, MGSTAR-LSTM, and TSR-MGSTAR, especially when the data contain nonlinear noise components. The results also show that the proposed hybrid model can tackle complex patterns on Spatio-temporal data containing trends, double seasonal, linear noise, nonlinear noise, and dependencies between locations.

Original languageEnglish
Title of host publication2021 9th International Conference on Information and Communication Technology, ICoICT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages582-587
Number of pages6
ISBN (Electronic)9781665404471
DOIs
Publication statusPublished - 3 Aug 2021
Event9th International Conference on Information and Communication Technology, ICoICT 2021 - Virtual, Yogyakarta, Indonesia
Duration: 3 Aug 20215 Aug 2021

Publication series

Name2021 9th International Conference on Information and Communication Technology, ICoICT 2021

Conference

Conference9th International Conference on Information and Communication Technology, ICoICT 2021
Country/TerritoryIndonesia
CityVirtual, Yogyakarta
Period3/08/215/08/21

Keywords

  • Hybrid
  • MGSTAR
  • Machine Learning
  • Multivariate
  • Spatio-Temporal
  • TSR

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