Abstract
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.
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Rosenthal, F., Lehner, W. (2011). Efficient In-Database Maintenance of ARIMA Models. In: Bayard Cushing, J., French, J., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2011. Lecture Notes in Computer Science, vol 6809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22351-8_36
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DOI: https://doi.org/10.1007/978-3-642-22351-8_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22350-1
Online ISBN: 978-3-642-22351-8
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