An adaptive local linear optimized radial basis functional neural network model for financial time series prediction. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Patra, A.; Das, S.; Mishra, S.; Senapati, M.
• Is Part Of: Neural computing & applications. Volume 28:Issue 1(2017)
• Page Start: 101
• Page End: 110
• Publication Date: 2017-01
• Subjects: Local linear radial basis functional neural network (LLRBFNN) -- Radial basis functional neural network (RBFNN) -- Multilayer perceptron (MLP) -- Recursive least square (RLS) -- Mean squared error (MSE); Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2039-0
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract For financial time series, the generation of error bars on the point of prediction is important in order to estimate the corresponding risk. In recent years, optimization techniques-driven artificial intelligence has been used to make time series approaches more systematic and improve forecasting performance. This paper presents a local linear radial basis functional neural network (LLRBFNN) model for classifying finance data from Yahoo Inc. The LLRBFNN model is learned by using the hybrid technique of backpropagation and recursive least square algorithm. The LLRBFNN model uses a local linear model in between the hidden layer and the output layer in contrast to the weights connected from hidden layer to output layer in typical neural network models. The obtained prediction result is compared with multilayer perceptron and radial basis functional neural network with the parameters being trained by gradient descent learning method. The proposed technique provides a lower mean squared error and thus can be considered as superior to other models. The technique is also tested on linear data, i.e., diabetic data, to confirm the validity of the result obtained from the experiment.