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Published in:
2011 | OriginalPaper | Chapter
Efficient In-Database Maintenance of ARIMA Models
Authors : Frank Rosenthal, Wolfgang Lehner
Published in: Scientific and Statistical Database Management
Publisher: Springer Berlin Heidelberg
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Forecasting is an important analysis task and there is a need of integrating time series models and estimation methods in database systems. The main issue is the computationally expensive maintenance of model parameters when new data is inserted. In this paper, we examine how an important class of time series models, the
AutoRegressive Integrated Moving Average
(ARIMA)
models, can be maintained with respect to inserts. Therefore, we propose a novel approach,
on-demand
estimation, for the efficient maintenance of maximum likelihood estimates from numerically implemented estimators. We present an extensive experimental evaluation on both real and synthetic data, which shows that our approach yields a substantial speedup while sacrificing only a limited amount of predictive accuracy.