A Data-Driven Model Selection Approach to Spatio-Temporal Prediction

Abstract

Spatio-temporal Predictive Queries encompass a spatio-temporal constraint, defining a region, a target variable, and an evaluation metric. The output of such queries presents the future values for the target variable computed by predictive models at each point of the spatio-temporal region. Unfortunately, especially for large spatio-temporal domains with millions of points, training temporal models at each spatial domain point is prohibitive. In this work, we propose a data-driven approach for selecting pre-trained temporal models to be applied at each query point. The chosen approach applies a model to a point according to the training and input time series similarity. The approach avoids training a different model for each domain point, saving model training time. Moreover, it provides a technique to decide on the best-trained model to be applied to a point for prediction. In order to assess the applicability of the proposed strategy, we evaluate a case study for temperature forecasting using historical data and auto-regressive models. Computational experiments show that the proposed approach, compared to the baseline, achieves equivalent predictive performance using a composition of pre-trained models at a fraction of the total computational cost.

Life Science Workflow Services (LifeSWS): Motivations and Architecture

“…LifeSWS capitalizes on our previous experience in developing major systems for scientific applications such as: polystores with CloudMdSQL [17], workflows with OpenAlea [30], model management with Gypscie [35] [38], querying data across distributed services with DfAnalyzer [32] and Provlake [33], monitoring and debugging applications implemented in big data frameworks such as Apache Spark [12], and debugging workflows with BugDoc [18] and VersionClimber [29].…”

“…A good example of a LifeSWS platform is Gypscie [38], which provides services to develop, share, improve and publish scientific artifacts (datasets, models, etc.). Gypscie's services are available through two different interfaces.…”