short-paper

HierTGAN: Hierarchical Time Series Generation with Aggregation Constraints

Authors:

Srini Rohan Gujulla Leel,

Puspita Majumdar,

Ankit KhairkarAuthors Info & Claims

CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)

Pages 217 - 221

https://doi.org/10.1145/3632410.3632444

Published: 04 January 2024 Publication History

Abstract

Generative models for time series data have been able to preserve the temporal dynamics of the original time series and are extremely successful in generating realistic synthetic data. However, in the real world, time series data can be disaggregated by various attributes of interest, thereby forming a hierarchical structure, often referred to as hierarchical time series data. Existing models for time series generation do not capture the structural dynamics (inter-level relationships of the hierarchy) of hierarchical time series data. Therefore, in this research, for the first time, we introduce HierTGAN, an auto-regressive generative adversarial network (GAN) for hierarchical time series generation. The proposed HierTGAN solves for an equivalent inter-level relationship within the embedding space generated by an autoencoder. Multiple experiments have been performed to evaluate the effectiveness of HierTGAN in generating realistic synthetic hierarchical time series data.

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Cited By

Qi XChai HYu LLi YWang Z(2024)Regional Features Conditioned Diffusion Models for 5G Network Traffic GenerationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691312(396-409)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691312

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CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)

January 2024

627 pages

ISBN:9798400716348

DOI:10.1145/3632410

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 January 2024

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CODS-COMAD 2024

CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)

January 4 - 7, 2024

Bangalore, India

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Cited By

Qi XChai HYu LLi YWang Z(2024)Regional Features Conditioned Diffusion Models for 5G Network Traffic GenerationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691312(396-409)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691312

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