Robust induction of process models from time-series data
Pat Langley, Dileep George, Stephen D. Bay, Kazumi Saito · 2003
In this paper, we revisit the problem of in-ducing a process model from time-series data. We illustrate this task with a realistic ecosys-tem model, review an initial method for its induction, then identify three challenges that require extension of this method. These in-clude dealing with unobservable variables, finding numeric conditions on processes, and preventing the creation of models that over-fit 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 ap-proach to inductive process modeling can ex-plain and predict time-series data from bat-teries on the International Space Station. In closing, we discuss related work and consider directions for future research. 1.