Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals

Ghazali, Rozaida; Hussain, Abir Jaafar; Liatsis, Panos

doi:10.1016/j.eswa.2010.09.037

Cited by 57 publications

(43 citation statements)

References 14 publications

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“…However, there is a special type of HONN called as Pi-Sigma neural network (PSNN) using less number of weights has been introduced by Shin and Ghosh [8] in 1991. The PSNN has been successfully employed solving several difficult problems including polynomial factorization [9], zeroing polynomials [10], classification [11,12], time series forecasting [13,14]. In this paper, a PSNN is considered for the task of short-term prediction of closing prices of some real stock market.…”

Section: Introductionmentioning

confidence: 99%

A Pi-Sigma Higher Order Neural Network for Stock Index Forecasting

Nayak

Misra²,

Behera

2014

Smart Innovation, Systems and Technologies

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Multilayer perceptron (MLP) has been found to be most frequently used model for stock market forecasting. MLP is characterized with black-box in nature and lack of providing a formal method of deriving ultimate structure of the model. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities that require less memory in terms of weights and nodes and have been utilized in many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper considered a HONN, called Pi-Sigma Neural Network (PSNN), for prediction of closing prices of five real stock markets. The tunable weights are optimized by Gradient Descent (GD) and a global search technique, Genetic Algorithm (GA). The model proves its superiority when trained with GA in terms of Average Percentage of Errors (APE).

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Section: Introductionmentioning

confidence: 99%

A Pi-Sigma Higher Order Neural Network for Stock Index Forecasting

Nayak

Misra²,

Behera

2014

Smart Innovation, Systems and Technologies

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“…Functional Link Neural Network is a class of HONNs created by Pao [7] and has been successfully used in many applications such as system identification [9][10][11][12][13][14], channel equalization [3], classification [15][16][17][18], pattern recognition [19,20] and prediction [21,22]. In this paper, we would discuss on the FLNN for the prediction task.…”

Section: Functional Link Neural Networkmentioning

confidence: 99%

“…In most previous researches, the learning algorithm used for training the FLNN is the Backpropagation (BP) [8,16,22,23,[25][26][27]. BP learning is developed by Rumelhart [28] in which the network is provided with examples of the inputs and desired outputs to be computed, and then the error (difference between actual and expected results) will be calculated.…”

Section: Flnn Learning Schemementioning

confidence: 99%

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

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Abstract. Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

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“…Since weights from hidden layer to the output are fixed at 1, the property of PSNN drastically reduces the training time. The applicability of this network was successfully applied for image processing (Hussain and Liatsis, 2002), time series prediction (Knowles, 2005;Ghazali et al, 2011), function approximation ( Shin & Ghosh, 1991-a;Shin & Ghosh, 1991-b), pattern recognition ( Shin & Ghosh, 1991-a), Cryptography (Song, 2008), and so forth.…”

Section: Pi-sigma Neural Network (Psnn)mentioning

confidence: 99%