Heuristic Induction of Rate-Based Process Models

Abstract 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 proportional to 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. Background and Motivation Reasoning about scientific domains is a high-level cognitive task that is widely viewed as requiring considerable intelligence. Over the past three decades, research on computational scientific discovery Much of the research in this area has focused on equation discovery, which has played a key role in the history of science and which has clear applications in many different disciplines. However, this task often arises in the early stages of a field's development, before theoretical knowledge supports the creation of explanatory models that account for observations in deeper terms. The task of explanatory model construction has received less attention from computational researchers, but there has been some progress on this topic. One important form of such tasks, known as inductive process modeling In the sections that follow, we review drawbacks of previous efforts on inductive process modeling, focusing on their computational expense and problems with parameter estimation. In response, we present a new approach to process model induction that adopts representational assumptions which let it carry out heuristic rather than exhaustive search through the space of model structures, making it both more efficient and more reliable. We also describe an implemented system that incorporates these ideas, and we report experimental studies that demonstrate its ability to identify relevant processes, handle noisy observations, and construct accurate models that relate many variables. We conclude by discussing our framework's relation to earlier work and proposing responses to its limitations. Critique of Process Modeling Research Research on inductive process modeling has made continual progress since its first appearance over a decade ago The first characteristic is that, even though process models are compositional in nature, current algorithms evaluate only complete model structures. This evaluation involves comparing a parameterized model's predictions with observed time series. Existing systems generate many model structures up to a given complexity level, then parameterize and evaluate each candidate in turn. This approach doe

Automated development of process time estimating models

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Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Inducing Hierarchical Process Models in Dynamic Domains

Research on inductive process modeling combines background knowledge with time-series data to construct explanatory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchical manner, and we describe HIPM, a system that carries out constrained search for hierarchical process models. We report experiments that suggest this approach produces more accurate and plausible models with less effort. We conclude by discussing related research and directions for future work