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This paper presents a novel approach to inductive process modeling, the task of constructing a quantitative account of dynamical behavior from time-series data and background knowledge. We review earlier work on this topic, noting its reliance on methods that evaluate entire model structures and use repeated simulation to estimate parameters, which together make severe computational demands. In response, we present an alternative method for process model induction that assumes each process has a rate, that this rate is determined by an algebraic expression, and that changes due to a process are directly proportionalto its rate. We describe RPM, an implemented system that incorporates these ideas, and we report analyses and experiments that suggest it scales well to complex domains and data sets. In closing, we discuss related research and outline ways to extend the framework.

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Inductive process modeling involves the construction of explanatory accounts for multivariate time series. As typically specified, background knowledge is available in the form of generic processes that serve as the building blocks for candidate model structures. In this paper, we present a more flexible approach that, when available processes are insufficient to construct an acceptable model, automatically produces new generic processes that let it complete the task. We describe FPM, a system that implements this idea by composing knowledge about algebraic rate expressions and about conceptual processes like predation and remineralization in ecology. We demonstrate empirically FPM's ability to construct new generic processes when necessary and to transfer them later to new modeling tasks. We also compare its failure-driven approach with a naive scheme that generates all possible processes at the outset. We conclude by discussing prior work on equation discovery and model construction, along with plans for additional research.