A skip-list approach for efficiently processing forecasting queries

13 years 4 months ago

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Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support prediction queries over time series data. Prediction relies on model building which can be too expensive to be practical if it is based on a large number of data points. We propose to use statistical tests of hypotheses to choose a proper subset of data points to use for a given prediction query interval. This involves two steps: choosing a proper history length and choosing the number of data points to use within this history. Further, we use an I/O conscious skip list data structure to provide samples of the original data set. Based on the statistics collected for a query workload, which we model as a probability mass function (PMF) over query intervals, we devise a randomized algorithm that selects a set of pre-built models (PM's) to construct, subject to some maintenance cost constraint when there are up...

Tingjian Ge, Stanley B. Zdonik

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