A novel model by evolving partially connected neural network for stock price trend forecasting

Expert Systems with Applications

Volumn 39, Issue 1, 2012, Pages 611-620

  Chang, Pei Chann ^a,b   Wang, Di Di ^a,b   Zhou, Chang Le ^b  

^a YUAN ZE UNIVERSITY   (Taiwan)

^b XIAMEN UNIVERSITY   (China)

Author keywords

Genetic algorithms; Neural network; Over fitting; Partially connected neural networks; Stock prediction

Indexed keywords

ACCURATE PREDICTION; ACTIVATION FUNCTIONS; FINANCIAL TIME SERIES; GRADIENT DESCENT ALGORITHMS; HIDDEN LAYERS; MULTIPLE REGRESSION ANALYSIS; NEURAL NETWORK TRAINING; OVER FITTING PROBLEM; OVERFITTING; PARTIALLY CONNECTED NEURAL NETWORK; PREDICTION ACCURACY; PROPOSED ARCHITECTURES; SIGMOID FUNCTION; STOCK PREDICTIONS; STOCK PRICE; TECHNICAL INDICATOR; TIME-SERIES DATA; TSK FUZZY SYSTEM;

EVOLUTIONARY ALGORITHMS; EXPERIMENTS; FINANCIAL DATA PROCESSING; FORECASTING; GENETIC ALGORITHMS; LEARNING ALGORITHMS; REGRESSION ANALYSIS; TIME SERIES;

NEURAL NETWORKS;

EID: 81855161579     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.07.051     Document Type: Article

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