Forecasting of Taiwan’s Gross Domestic Product using the Novel Nonlinear Grey Bernoulli Model with ANN Error Correction

Chen Ci Hsin Ph

doi:10.4172/2375-4389.1000177

Abstract

Nonlinear Grey Bernoulli Model is proposed to enhance the prediction accuracy. In this study, artificial neural network (ANN) is used to modify the residual error of NGBM. Then, ANN error plus original forecasted value is a new estimated value. The newly proposed method termed NGBM (1,1) with ANN error correction is used to forecast Taiwan’s gross domestic product (GDP). The results show the proposed method is more accurate than NGBM and is proven to be effective in forecasting.

Highlights

Grey theory [1] has been proposed over 30 years
(9) original forecast value is obtained by Nonlinear Grey Bernoulli Model (NGBM); the estimated forecast error is calculated by artificial neural network (ANN)
Grey forecasting is suitable for forecasting gross domestic product (GDP)

Summary

Introduction

Grey theory [1] has been proposed over 30 years. Great endeavor has been devoted to increase the forecasting precision. Hsu and Wen [2] modified original GM (1,1) models are improved by using residual modifications with Markov chain sign estimations. Hsu and Chen [3] improved grey GM (1,1) model, using a technique that combines residual modification with artificial neural network sign estimation, is proposed. Hsu [4] applied three residual modification models to enhance the forecasting results. A nonlinear grey forecasting model which was termed NGBM was proposed. Chen et al [7] proposed NGBM to forecast the foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM. Zhang [10] used a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM (1,1) is proposed.

Methodology

Conclusions