Abstract
We introduce intervention optimization as a new area of exploration for data mining research. Interventions are events designed to impact a corresponding time series. The task is to maximize the impact of such events by training a model on historical data. We propose PRIMER as a new regression-rule learning system for identifying sets of event features that maximize impact. PRIMER is for use when domain experts with knowledge of the intervention can specify a transfer function, or the form of the expected response in the time series. PRIMER’s objective function includes the goodness-of-fit of the average response of covered events to the transfer function. Incorporating domain knowledge in this way makes PRIMER robust to over-fitting on noise or spurious responses. PRIMER is designed to produce interpretable results, improving on the interpretability of even competing regression-rule systems for this task. It also has fewer and more intuitive parameters than competing rule-based systems. Empirically, we show that PRIMER is competitive with state-of-the-art regression techniques in a large-scale event study modeling the impact of insider trading on intra-day stock returns.
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Acknowledgments
This work is supported by Chevron USA, Inc. under the joint project Center for Interactive Smart Oilfield Technologies (CiSoft), at the University of Southern California.
We would also like to thank Dr. Frederik Janssen for providing support with the SeCoReg and Dynamic Reduction to Regression algorithms.
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Harris, G., Panangadan, A., Prasanna, V.K. (2016). PRIMER – A Regression-Rule Learning System for Intervention Optimization. In: Alferes, J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds) Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science(), vol 9718. Springer, Cham. https://doi.org/10.1007/978-3-319-42019-6_20
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DOI: https://doi.org/10.1007/978-3-319-42019-6_20
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