L. Kegel, M. Hahmann, and W. Lehner. Proceedings of the 29th International Conference on Scientific and Statistical Database Management, New York, NY, USA, Association for Computing Machinery, (2017)
DOI: 10.1145/3085504.3085507
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.
%0 Conference Paper
%1 10.1145/3085504.3085507
%A Kegel, Lars
%A Hahmann, Martin
%A Lehner, Wolfgang
%B Proceedings of the 29th International Conference on Scientific and Statistical Database Management
%C New York, NY, USA
%D 2017
%I Association for Computing Machinery
%K analysis, analytics business hypothetical query, scenario, series time what-if
%R 10.1145/3085504.3085507
%T Generating What-If Scenarios for Time Series Data
%U https://doi.org/10.1145/3085504.3085507
%X 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.
%@ 9781450352826
@inproceedings{10.1145/3085504.3085507,
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.},
added-at = {2024-10-02T10:38:17.000+0200},
address = {New York, NY, USA},
articleno = {3},
author = {Kegel, Lars and Hahmann, Martin and Lehner, Wolfgang},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2806263060ddd933b6a1ead17b0992c80/scadsfct},
booktitle = {Proceedings of the 29th International Conference on Scientific and Statistical Database Management},
doi = {10.1145/3085504.3085507},
interhash = {09d24daa30e58549239293315b417c7b},
intrahash = {806263060ddd933b6a1ead17b0992c80},
isbn = {9781450352826},
keywords = {analysis, analytics business hypothetical query, scenario, series time what-if},
location = {Chicago, IL, USA},
numpages = {12},
publisher = {Association for Computing Machinery},
series = {SSDBM '17},
timestamp = {2024-10-02T10:38:17.000+0200},
title = {Generating What-If Scenarios for Time Series Data},
url = {https://doi.org/10.1145/3085504.3085507},
year = 2017
}
