Genetic Programming Prediction of Stock Prices
Computational Economics
Improving Technical Analysis Predictions: An Application of Genetic Programming
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent stock trading system by turning point confirming and probabilistic reasoning
Expert Systems with Applications: An International Journal
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Neural Networks
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In developing grant proposals for funding agencies like NIH or NSF, it is often important to determine whether a research topic is gaining momentum -- where by 'momentum' we mean the rate of change of a certain measure such as popularity, impact or significance -- to evaluate whether the topic is more likely to receive grants. Analysis of data about past grant awards reveals interesting patterns about successful grant topics, suggesting it is sometimes possible to measure the degree to which a given research topic has 'increasing momentum'. In this paper, we develop a framework for quantitative modeling of the funding momentum of a project, based on the momentum of the individual topics in the project. This momentum follows certain patterns that rise and fall in a predictable fashion. To our knowledge, this is the first attempt to quantify the momentum of research topics or projects.