NDLI: Forecasting the data cube: A model configuration advisor for multi-dimensional data sets

Content Provider	IEEE Xplore Digital Library
Author	Fischer, U. Schildt, C. Hartmann, C. Lehner, W.
Copyright Year	2013
Description	Author affiliation: Database Technol. Group, Dresden Univ. of Technol., Dresden, Germany (Fischer, U.; Schildt, C.; Hartmann, C.; Lehner, W.)
Abstract	Forecasting time series data is crucial in a number of domains such as supply chain management and display advertisement. In these areas, the time series data to forecast is typically organized along multiple dimensions leading to a high number of time series that need to be forecasted. Most current approaches focus only on selection and optimizing a forecast model for a single time series. In this paper, we explore how we can utilize time series at different dimensions to increase forecast accuracy and, optionally, reduce model maintenance overhead. Solving this problem is challenging due to the large space of possibilities and possible high model creation costs. We propose a model configuration advisor that automatically determines the best set of models, a model configuration, for a given multi-dimensional data set. Our approach is based on a general process that iteratively examines more and more models and simultaneously controls the search space depending on the data set, model type and available hardware. The final model configuration is integrated into F2DB, an extension of PostgreSQL, that processes forecast queries and maintains the configuration as new data arrives. We comprehensively evaluated our approach on real and synthetic data sets. The evaluation shows that our approach significantly increases forecast query accuracy while ensuring low model costs.
Sponsorship	IEEE Comput. Soc.
Starting Page	853
Ending Page	864
File Size	596884
Page Count	12
File Format	PDF
ISBN	9781467349093
ISSN	10636382
e-ISBN	9781467349109
e-ISBN	9781467349086
DOI	10.1109/ICDE.2013.6544880
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Predictive models Data models Accuracy Cities and towns Forecasting Numerical models
Content Type	Text
Resource Type	Article

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