Processing forecasting queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A skip-list approach for efficiently processing forecasting queries
Proceedings of the VLDB Endowment
Forecasting high-dimensional data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Indexing forecast models for matching and maintenance
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
Sample-based forecasting exploiting hierarchical time series
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Model-based integration of past & future in TimeTravel
Proceedings of the VLDB Endowment
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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.