Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We describe the Fa system where users and applications can pose declarative forecasting queries—both onetime queries and continuous queries—and get forecasts in real-time along with accuracy estimates. Fa supports efficient algorithms to generate execution plans automatically for forecasting queries from a novel plan space comprising operators for transforming data, learning statistical models from data, and doing inference using the learned models. In addition, Fa supports adaptive query-processing algorithms that adapt plans for continuous forecasting queries to the time-varying properties of input data streams. We report an extensive experimental evaluation of Fa using synthetic datasets, datasets collected on a testbed, and two real datasets from production settings. Our experiments give interesting insights on plans for forecasting queries, and demonstrate the effectiveness and scalability of our planselection algorithms. 1
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