Sample-based forecasting exploiting hierarchical time series
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Optimizing notifications of subscription-based forecast queries
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Model-based integration of past & future in TimeTravel
Proceedings of the VLDB Endowment
pEDM: online-forecasting for smart energy analytics
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Forecasts are important to decision-making and risk assessment in many domains. Since current database systems do not provide integrated support for forecasting, it is usually done outside the database system by specially trained experts using forecast models. However, integrating model-based forecasting as a first-class citizen inside a DBMS speeds up the forecasting process by avoiding exporting the data and by applying database-related optimizations like reusing created forecast models. It especially allows subsequent processing of forecast results inside the database. In this demo, we present our prototype F2DB based on PostgreSQL, which allows for transparent processing of forecast queries. Our system automatically takes care of model maintenance when the underlying dataset changes. In addition, we offer optimizations to save maintenance costs and increase accuracy by using derivation schemes for multidimensional data. Our approach reduces the required expert knowledge by enabling arbitrary users to apply forecasting in a declarative way.