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research-article

Generating What-If Scenarios for Time Series Data

Authors:

Martin Hahmann,

Wolfgang LehnerAuthors Info & Claims

SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management

Article No.: 3, Pages 1 - 12

https://doi.org/10.1145/3085504.3085507

Published: 27 June 2017 Publication History

Abstract

Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features.

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Cited By

Ieva SLoconte DLoseto GRuta MScioscia FMarche DNotarnicola M(2024)A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital TwinsSmart Cities10.3390/smartcities70601217:6(3095-3120)Online publication date: 24-Oct-2024
https://doi.org/10.3390/smartcities7060121
Sonenberg NVolodina VChallenor PSmith J(2023)Using infinite server queues with partial information for occupancy predictionJournal of the Operational Research Society10.1080/01605682.2023.218900275:2(262-277)Online publication date: 30-Mar-2023
https://doi.org/10.1080/01605682.2023.2189002
Li LLi FKang Y(2023)Forecasting Large Collections of Time Series: Feature-Based MethodsForecasting with Artificial Intelligence10.1007/978-3-031-35879-1_10(251-276)Online publication date: 21-Sep-2023
https://doi.org/10.1007/978-3-031-35879-1_10
Show More Cited By

Index Terms

Generating What-If Scenarios for Time Series Data

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Information

Published In

cover image ACM Other conferences

SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management

June 2017

373 pages

ISBN:9781450352826

DOI:10.1145/3085504

General Chair:
Alok Choudhary,
Program Chair:
Kesheng Wu,
Publications Chair:
Bin Dong

Copyright © 2017 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 ACM 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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Northwestern University: Northwestern University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2017

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Horizon 2020

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SSDBM '17

SSDBM '17: 29th International Conference on Scientific and Statistical Database Management

June 27 - 29, 2017

IL, Chicago, USA

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Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

Ieva SLoconte DLoseto GRuta MScioscia FMarche DNotarnicola M(2024)A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital TwinsSmart Cities10.3390/smartcities70601217:6(3095-3120)Online publication date: 24-Oct-2024
https://doi.org/10.3390/smartcities7060121
Sonenberg NVolodina VChallenor PSmith J(2023)Using infinite server queues with partial information for occupancy predictionJournal of the Operational Research Society10.1080/01605682.2023.218900275:2(262-277)Online publication date: 30-Mar-2023
https://doi.org/10.1080/01605682.2023.2189002
Li LLi FKang Y(2023)Forecasting Large Collections of Time Series: Feature-Based MethodsForecasting with Artificial Intelligence10.1007/978-3-031-35879-1_10(251-276)Online publication date: 21-Sep-2023
https://doi.org/10.1007/978-3-031-35879-1_10
Wang CWu KZhou TYu GCai Z(2022)TSAGen: Synthetic Time Series Generation for KPI Anomaly DetectionIEEE Transactions on Network and Service Management10.1109/TNSM.2021.309878419:1(130-145)Online publication date: Mar-2022
https://doi.org/10.1109/TNSM.2021.3098784
Naveed MHashmi UTajved NSultan NImran A(2022)Assessing Deep Generative Models on Time Series Network DataIEEE Access10.1109/ACCESS.2022.317790610(64601-64617)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177906
Kang YHyndman RLi F(2020)GRATISStatistical Analysis and Data Mining10.1002/sam.1146113:4(354-376)Online publication date: 2-Jul-2020
https://dl.acm.org/doi/10.1002/sam.11461
Hahmann MHartmann CKegel LLehner W(2019)Large-Scale Time Series AnalyticsDatenbank-Spektrum10.1007/s13222-018-00304-5Online publication date: 21-Jan-2019
https://doi.org/10.1007/s13222-018-00304-5
Kegel LHahmann MLehner WSacharidis DGamper JBöhlen M(2018)Feature-based comparison and generation of time seriesProceedings of the 30th International Conference on Scientific and Statistical Database Management10.1145/3221269.3221293(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3221269.3221293
Lehner W(2017)The Dresden Database Systems GroupACM SIGMOD Record10.1145/3156655.315666546:3(36-41)Online publication date: 31-Oct-2017
https://dl.acm.org/doi/10.1145/3156655.3156665

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