Forecasting with Multiple Seasonality

Tianyang Xie; Jie Ding

doi:10.1109/BigData50022.2020.9378072

Forecasting with Multiple Seasonality

Tianyang Xie, Jie Ding

Statistics (Twin Cities)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao 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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	240-245
Number of pages	6
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378072
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

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

Publisher link

10.1109/BigData50022.2020.9378072

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Xie, T., & Ding, J. (2020). Forecasting with Multiple Seasonality. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 240-245). Article 9378072 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9378072

Forecasting with Multiple Seasonality. / Xie, Tianyang; Ding, Jie.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao 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. Institute of Electrical and Electronics Engineers Inc., 2020. p. 240-245 9378072 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xie, T & Ding, J 2020, Forecasting with Multiple Seasonality. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378072, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 240-245, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378072

Xie T, Ding J. Forecasting with Multiple Seasonality. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 240-245. 9378072. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378072

Xie, Tianyang ; Ding, Jie. / Forecasting with Multiple Seasonality. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao 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. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 240-245 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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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.",

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AU - Ding, Jie

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A2 - Wu, Xintao

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A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

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A2 - Saltz, Jeff

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ER -

Forecasting with Multiple Seasonality

Abstract

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Keywords

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