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J. Schott and J. Kalita

Jeff Schott & Jugal Kalita

Intelligent Systems in Accounting, Finance and Management: An International Journal2011article
ABDC B
Weight
0.26

Abstract

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.

Cite this paper

@article{jeff2011,
  title        = {{J. Schott and J. Kalita}},
  author       = {Jeff Schott & Jugal Kalita},
  journal      = {Intelligent Systems in Accounting, Finance and Management: An International Journal},
  year         = {2011},
}

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J. Schott and J. Kalita

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.