Forecasting with Multiple Seasonality

Tianyang Xie, Jie Ding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Several modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. In particular, modeling time series with multiple seasonality is a challenging task with relatively few discussions. In this paper, we propose a two-stage method for predicting time series with multi-seasonality, which does not require predetermined seasonality periods. In the first stage, we generalize the classical seasonal autoregressive moving average model to multi-seasonality scenarios. In the second stage, we utilize an appropriate criterion for lag order selection. Simulation and empirical studies show the excellent predictive performance of our method, especially when compared with a recently popular 'Facebook Prophet' model for time series.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages240-245
Number of pages6
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Bibliographical note

Funding Information:
The work was supported by the U.S. Army Research Office under grant number W911NF-20-1-0222.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Model selection
  • Multiple seasonality
  • Nowcasting
  • Time series

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