Self-organized language modeling for speech recognition
Readings in speech recognition
Combining artificial neural networks and statistics for stock-market forecasting
CSC '93 Proceedings of the 1993 ACM conference on Computer science
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Learning models for English speech recognition
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Bond rating using support vector machine
Intelligent Data Analysis
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Fuzzy portfolio optimization under downside risk measures
Fuzzy Sets and Systems
Artificial neural networks in smart homes
Designing Smart Homes
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
WSEAS Transactions on Information Science and Applications
A new approach to the prediction of passenger flow in a transit system
Computers & Mathematics with Applications
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Stock price prediction based on procedural neural networks
Advances in Artificial Neural Systems
A hybrid fuzzy intelligent agent-based system for stock price prediction
International Journal of Intelligent Systems
Probability tree based passenger flow prediction and its application to the Beijing subway system
Frontiers of Computer Science: Selected Publications from Chinese Universities
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This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted average to generate a 'one-day-ahead' forecast. This paper uses a similar approach to identify data patterns by using the HMM and then uses fuzzy logic to obtain a forecast value. The HMM's log-likelihood for each of the input data vectors is used to partition the dataspace. Each of the divided dataspaces is then used to generate a fuzzy rule. The fuzzy model developed from this approach is tested on stock market data drawn from different sectors. Experimental results clearly show an improved forecasting accuracy compared to other forecasting models such as, ARIMA, artificial neural network (ANN) and another HMM-based forecasting model.