Robust Induction of Process Models from Time-Series Data

Pat Langley, Dileep George, Stephen Bay, and Kazumi Saito

In this paper, we revisit the problem of inducing a process model from time-series data. We illustrate this task with a realistic ecosystem model, review an initial method for its induction, then identify three challenges that require extension of this method. These include dealing with unobservable variables, finding numeric conditions on processes, and preventing the creation of models that overfit the training data. We describe responses to these challenges and present experimental evidence that they have the desired effects. After this, we show that this extended approach to inductive process modeling can explain and predict time-series data from batteries on the International Space Station. In closing, we discuss related work and consider directions for future research.


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