Robust Induction of Process Models from Time-Series Data
Robust Induction of Process Models from Time-Series Data
复制标题
从时间序列数据稳健归纳过程模型
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Kazumi Saito
中科院分区：
文献类型：
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作者：
P. Langley;Dileep George;Stephen D. Bay;Kazumi Saito
在这篇文章中，我们重温了从时间序列数据中归纳过程模型的问题。我们用一个现实的生态系统模型来说明这一任务，回顾了一种初步的诱导方法，然后确定了需要扩展该方法的三个挑战。这些措施包括处理不可观测的变量，找到过程的数值条件，以及防止创建与训练数据过度匹配的模型。我们描述了对这些挑战的反应，并提出了实验证据，证明它们具有预期的效果。在此之后，我们展示了这种扩展的感应过程建模方法可以解释和预测国际空间站上电池的时间序列数据。最后，我们讨论了相关工作，并考虑了未来研究的方向。
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.