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.|
|Number of pages||6|
|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
|Name||Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020|
|Conference||8th IEEE International Conference on Big Data, Big Data 2020|
|Period||12/10/20 → 12/13/20|
Bibliographical noteFunding Information:
The work was supported by the U.S. Army Research Office under grant number W911NF-20-1-0222.
© 2020 IEEE.
- Model selection
- Multiple seasonality
- Time series