Universal approximation using radial-basis-function networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Fuzzy and Neural Hybrid Expert Systems: Synergetic AI
IEEE Expert: Intelligent Systems and Their Applications
An adaptive hierarchical fuzzy logic system for modelling of financial systems: Research Articles
International Journal of Intelligent Systems in Accounting and Finance Management
Flood forecasting using radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary fuzzy neural networks for hybrid financial prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Weather analysis using ensemble of connectionist learning paradigms
Applied Soft Computing
A genetic algorithm for maximum-weighted tree matching problem
Applied Soft Computing
Forecasting stock market based on price trend and variation pattern
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Analysis of decision making factors for equity investment by DEMATEL and Analytic Network Process
Expert Systems with Applications: An International Journal
Intelligence decision trading systems for stock index
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
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Technical analysis of stocks mainly focuses on the study of irregularities, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making.