Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference

No abstract is available for this paper.

• Publication year

  1998

• Venue

  Unknown venue

• Publication date

  1998-10-15

• Fields of study

  Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Neural Networks: A Comprehensive Foundation

  1998cited by this paper
• Kolmogorov's theorem

  1998cited by this paper
• Can recurrent neural networks learn natural language grammars?

  1996cited by this paper
• Neural networks for speech and sequence recognition

  1996cited by this paper
• Rule extraction by successive regularization

  1996cited by this paper
• Extraction of rules from discrete-time recurrent neural networks

  1996cited by this paper
• Symbolic Representation of Neural Networks

  1996cited by this paper
• Statistical mechanics of nonlinear nonequilibrium financial markets: Applications to optimized trading

  1996cited by this paper
• Self-Organizing Maps

  1995influential reference
• Economic forecasting : challenges and neural network solutions

  1995cited by this paper
• Neural networks

  1995cited by this paper
• An Overview of Predictive Learning and Function Approximation

  1994cited by this paper
• Discrete recurrent neural networks for grammatical inference

  1994cited by this paper
• Time Series Prediction : Forecasting The Future And Understanding The Past

  1994cited by this paper
• Template-Based Algorithms for Connectionist Rule Extraction

  1994cited by this paper
• Universal approximation bounds for superpositions of a sigmoidal function

  1993cited by this paper
• Strong Universal Consistency of Neural Network Classifiers

  1993cited by this paper
• Extraction, Insertion and Refinement of Symbolic Rules in Dynamically Driven Recurrent Neural Networks

  1993cited by this paper
• THE EXTRACTION OF REFINED RULES FROM KNOWLEDGE BASED NEURAL NETWORKS

  1993cited by this paper
• On the computational power of neural nets

  1992cited by this paper
• Induction of Finite-State Languages Using Second-Order Recurrent Networks

  1992cited by this paper
• Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks

  1992cited by this paper
• Predicting sunspots and exchange rates with connectionist networks

  1991cited by this paper
• Rule Induction through Integrated Symbolic and Subsymbolic Processing

  1991cited by this paper
• Kolmogorov's Theorem Is Relevant

  1991cited by this paper
• Extracting and Learning an Unknown Grammar with Recurrent Neural Networks

  1991cited by this paper
• ADAPTIVE CONTROL PROCESSES

  1990cited by this paper
• A Neural Expert System with Automated Extraction of Fuzzy If-Then Rules

  1990cited by this paper
• Note on Learning Rate Schedules for Stochastic Optimization

  1990cited by this paper
• The multilayer perceptron as an approximation to a Bayes optimal discriminant function

  1990cited by this paper
• Learning from hints in neural networks

  1990cited by this paper
• Finite State Automata and Simple Recurrent Networks

  1989cited by this paper
• Higher Order Recurrent Networks and Grammatical Inference

  1989cited by this paper
• Modelling Financial Time Series

  1987cited by this paper
• Large Automatic Learning, Rule Extraction, and Generalization

  1987cited by this paper
• Detecting strange attractors in turbulence

  1981cited by this paper
• J.E.Hopcroft, J.D. Ullman 著, "Introduction to Automata Theory, Languages, and Computation", Addison-Wesley, A5変形版, X+418, \6,670, 1979

  1980cited by this paper
• Forecasting Economic Time Series

  1979cited by this paper
• Introduction to Automata Theory, Languages and Computation

  1979cited by this paper
• EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*

  1970cited by this paper
• FAMA, . The Behavior of Stock-Market Prices, Journal of Business, , .

  1965cited by this paper
• V. Adaptive Control Processes

  1964cited by this paper
• On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer's Sunspot Numbers

  year unknowncited by this paper

• Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice

  2018cites this paper
• A New Generic Architecture For Time Series Prediction *

  2012cites this paper
• A COMPARISON BETWEEN HYBRID APPROACHES OF ANN AND ARIMA FOR INDIAN STOCK TREND FORECASTING

  2010cites this paper
• Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network

  2009cites this paper
• Application of machine learning techniques for supply chain demand forecasting

  2008cites this paper
• The informational efficiency and the financial crashes

  2008cites this paper
• WEIGHTS OPTIMIZATION OF NEURAL NETWORK VIA IMPROVED BCO APPROACH

  2008cites this paper
• A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets

  2007cites this paper
• A multivariate neuro-fuzzy system for foreign currency risk management decision making

  2007cites this paper
• Extended Genetic Algorithm for Tuning a Multiple Classifier System

  2006cites this paper
• Genetic design of discrete dynamical basis networks that approximate data sequences and functions

  2004cites this paper
• Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures

  2004cites this paper
• Stock market prediction using artificial neural networks with optimal feature transformation

  2004cites this paper
• Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting

  2004cites this paper
• Forecast of Short Term Trends in Stock Exchange using Fuzzy Rules and Neural Networks on Multiresolution Processed Signals

  2002cites this paper
• Soft modeling of III-defined systems

  2002cites this paper
• The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction

  2001cites this paper
• Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm

  2001cites this paper
• A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders

  2001cites this paper
• Nonlinear and Noisy Time Series Prediction Using a Hybrid Nonlinear Neural Predictor

  2000cites this paper
• Energy management engineering : a predictive energy management system incorporating an adaptive neural network for the direct heating of domestic and industrial fluid mediums.

  2000cites this paper
• Recurrent neural networks for time-series prediction.

  2000cites this paper
• Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

  2000cites this paper
• The National Library of Australia

  2000cites this paper
• Neural Networks for Economic Forecasting

  1999cites this paper
• Simultaneous optimization method of feature transformation and weighting for artificial neural networks using genetic algorithm: application to Korean stock market

  1999cites this paper
• References

  year unknowncites this paper