An Artificial Neural Network for Data Forecasting Purposes

Catalina Lucia Cocianu,Hakob Grigoryan

doi:10.12948/issn14531305/19.2.2015.04

Catalina Lucia Cocianu, Hakob Grigoryan

Open Access

https://doi.org/10.12948/issn14531305/19.2.2015.04

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Abstract

Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of artificial neural networks (ANN) in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward neural architecture: the nonlinear autoregressive network with exogenous inputs. The network training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE) measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial data.

Highlights

Modelling and predicting volatility in banking sector
In the second section of the paper, we briefly present the Auto-Regressive Integrated Moving Average (ARIMA) model for prediction
The artificial neural networks (ANN)-based strategy applied for data forecasting is analysed against the ARIMA model, and a comparative analysis of these models is described in the fourth section of the paper

Summary

Introduction

Modelling and predicting volatility in banking sector. The paper is organized as follows. In the second section of the paper, we briefly present the ARIMA model for prediction. The ANN-based strategy applied for data forecasting is analysed against the ARIMA model, and a comparative analysis of these models is described in the fourth section of the paper. One of the most commonly used methods in forecasting ARMA(p,q) processes is the class of the recursive techniques for computing best linear predictors (the Durbin-Levison algorithm, the Innovations algorithm etc.). In the following we describe the recursive prediction method using the Innovation algorithm. We denote by Hn= sp {X1,X2,...,Xn}, n 1 the closed linear subspace generated by X1, X2, ..., Xn and let is an ARMA(p,q) process if is stationary and for each t the following relation holds where stands for the projection of. The equation (7) gives the coefficients of the innovations, in the orthogonal expansion (6), which is simple to use and, in the case of ARMA(p,q) processes, can be further simplified [15]

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