NDLI: Challenges for Context-Driven Time Series Forecasting

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Challenges for Context-Driven Time Series Forecasting

Content Provider	ACM Digital Library
Author	Ulbricht, Robert Donker, Hilko Hartmann, Claudio Hahmann, Martin Lehner, Wolfgang
Copyright Year	2016
Abstract	Predicting time series is a crucial task for organizations, since decisions are often based on uncertain information. Many forecasting models are designed from a generic statistical point of view. However, each real-world application requires domain-specific adaptations to obtain high-quality results. All such specifics are summarized by the term of context. In contrast to current approaches, we want to integrate context as the primary driver in the forecasting process. We introduce context-driven time series forecasting focusing on two exemplary domains: renewable energy and sparse sales data. In view of this, we discuss the challenge of context integration in the individual process steps.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISSN	19361955
e-ISSN	19361963
DOI	10.1145/2896822
Journal	Journal of Data and Information Quality (JDIQ)
Volume Number	7
Issue Number	1-2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Uncertain data Forecast evaluation Model selection Renewable energy Sales data
Content Type	Text
Resource Type	Article
Subject	Information Systems and Management Information Systems

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