ARMemNet: Autoregressive memory networks for multivariate time series forecasting

Jinuk Park, Chanhee Park, Hongchan Roh, Sanghyun Park

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

Abstract

Recently, several studies show the powerful capability of neural networks to capture non-linear features from time series which have multiple seasonal patterns. However, existing methods rely on convolution kernels implicitly, hence neglect to capture strong long-term patterns and lack interpretability. In this paper, we propose a memory-augmented neural network named AutoRegressive Memory Network (ARMemNet) for multivariate time series forecasting. ARMemNet utilizes memory components to explicitly encode intense long-term patterns. Furthermore, each encoder is designed to leverage inherently essential autoregressive property to represent short-term patterns. In experiments on real-world dataset, ARMemNet outperforms existing baselines and validates effectiveness of memory components for complex seasonality which is prevalent in time series datasets.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
PublisherAssociation for Computing Machinery
Pages1094-1097
Number of pages4
ISBN (Electronic)9781450381048
DOIs
Publication statusPublished - 2021 Mar 22
Event36th Annual ACM Symposium on Applied Computing, SAC 2021 - Virtual, Online, Korea, Republic of
Duration: 2021 Mar 222021 Mar 26

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference36th Annual ACM Symposium on Applied Computing, SAC 2021
CountryKorea, Republic of
CityVirtual, Online
Period21/3/2221/3/26

Bibliographical note

Funding Information:
is work was supported by Institute of Information ? communications Technology Planning ? Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2017–0–00477)

Publisher Copyright:
© 2021 Owner/Author.

All Science Journal Classification (ASJC) codes

  • Software

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