The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Recursive neural networks for associative memory
Recursive neural networks for associative memory
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Stock market prediction with multiple classifiers
Applied Intelligence
A modified Hopfield auto-associative memory with improved capacity
IEEE Transactions on Neural Networks
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The focus of this paper is to describe an application of a specific type of recurrent neural networks (RNN) in the domain of the technical analysis of the stock market. Said RNN is used as a tool that provides statistical information based on the historical time series processed in the training stage. In order to address certain questions of interest from the field of technical analysis of the financial markets, we introduce a set of constraints on the convergence of the state vector during the process of retrieval. Such approach retains its high computational efficiency notable in the unmodified version of the network, while it also provides an intuitive visualisation of the process of training and retrieval and thus facilitates an in-depth analysis of the system. We also present the pilot applications of exploiting information extracted from given time series to support an informed decision in an environment of real-time application.