Real-time forecasting and visualization toolkit for multi-seasonal time series

Jinduan Chen, Dominic L. Boccelli

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Many environmental data sets are driven by multiple superimposed periods, yet most time series analysis software packages only support single-seasonality. The objective of this research was to develop a software toolkit utilizing multi-seasonal Autoregressive Integrated (msARI) models. A toolkit in MATLAB was developed for msARI-based identification, estimation, forecasting, and visualization. In the toolkit, an adaptive forecasting routine uses a continual event loop for real-time data acquisition and parameter re-estimation. A statistical quality control algorithm monitors model performance and re-estimates parameters when necessary. A set of visualization tools provide animated graphical representations of forecasts, prediction intervals and key performance metrics. The toolkit was applied to three case studies: electricity demands, water demands, and sewer flows. The analysis of the results demonstrated that the explicit modeling of multi-seasonality improved model predictions. Therefore, the msARI software presents a promising tool for modeling and predicting real-time data series.

Original languageEnglish (US)
Pages (from-to)244-256
Number of pages13
JournalEnvironmental Modelling and Software
StatePublished - Jul 2018
Externally publishedYes


  • Autocorrelation
  • Forecasting
  • Seasonality
  • Time series
  • Visualization

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling


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