Indexing forecast models for matching and maintenance

Forecasts are important to decision-making and risk assessment in many domains. There has been recent interest in integrating forecast queries inside a DBMS. Answering a forecast query requires the creation of forecast models. Creating a forecast model is an expensive process and may require several scans over the base data as well as expensive operations to estimate model parameters. However, if forecast queries are issued repeatedly, answer times can be reduced significantly if forecast models are reused. Due to the possibly high number of forecast queries, existing models need to be found quickly. Therefore, we propose a model index that efficiently stores forecast models and allows for the efficient reuse of existing ones. Our experiments illustrate that the model index shows a negligible overhead for update transactions, but it yields significant improvements during query execution