J. Schott and J. Kalita
Jeff Schott, Jugal Kumar Kalita · 2011
This paper describes a framework that utilizes an adaptive-network-based fuzzy inference system to perform user-constrained pattern recognition on time-series data. Using a customizable fuzzy logic grammar, the architecture allows an analyst to capture domain expertise in a context-relevant manner. Fuzzy logic rules constructed by the analyst are used to perform feature extraction and influence the training of a neural network to perform pattern recognition. We demonstrate that the architecture is capable of performing noise-tolerant searches across multiple features on large volumes of time-series data. The experiments presented here are from the domain of stock analysis. We are able to create simple rule sets automatically to search a data warehouse of stocks to select stocks that exhibit desirable behaviours. Copyright © 2011 John Wiley & Sons, Ltd.