Print ISSN: 2204-1990

Online ISSN: 1323-6903

Keywords : ANN


FORECASTING USING NEURAL NETWORKS AND STOCHASTIC MODELS ON DAY OF THE WEEK EFFECT: A CASE STUDY OF KSE 100 INDEX

Azhar Ali Marri, Mir G.H.Talpur

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 5, Pages 2385-2395
DOI: 10.47750/cibg.2021.27.05.116

The main objective of this research was to determine the day of the week effect in Karachi stock exchange (KSE) 100 Index. The problems of financial market structure is analyzed and forecasted by many statistical models. For example Auto regressive integrated moving average (ARIMA) model and artificial neural network (ANN) models. In this research day of the week effect was investigated Wednesday found significant and Monday was noted not significant. 15 step ahead prediction of Wednesday was observed through ARIMA model and ANN models. The coefficient of correlation for actual and forecasted values was perceived 0.8871 by ARIMA model and 0.924 by ANN model. Power of accuracy was displayed 88.765% by ARIMA model and 97.18% by ANN model. Keywords; 

ESTIMATION OF COMMERCIAL AIRLINE TRAFFIC WITH ECONOMIC GROWTH INDICATORS

Erdal DURSUN; Cankut AYDIN; Ertan ÇINAR; Cengiz SERTKAYA

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 2, Pages 5683-5704
DOI: 10.47750/cibg.2021.27.02.572

In recent days, it is crucial to calculate the volume of air traffic in line with the economic indicators of the countries and to make the necessary plans to increase efficiency and productivity according to this density type. Although commercial airline traffic is known to be associated with economic growth indicators, the relationship between them cannot be expressed numerically. The purpose of this research is to form an Artificial Neural Network (ANN) -based model to reveal and estimate its relationship with commercial airline traffic, taking as reference economical growth parameters like; the country's Gross National Product (GDP), import and export data. In the ANN model created in the study, gross domestic product, import and export volume values are used for the estimation of commercial airline traffic. Simulation results were analyzed using correlation coefficient (R) determination coefficient (R ^ 2) and mean absolute percent error (MAPE) evaluation methods. It has been seen that the developed model is a successful model that can be used in the prediction of commercial airline traffic.

GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING

Dr. J Rajaram, D Navya, E Krishna, G Harika

Journal of Contemporary Issues in Business and Government, 2020, Volume 26, Issue 3, Pages 106-112
DOI: 10.47750/cibg.2020.26.03.013

This Paper introduces the implementation of different supervised learning techniques for producing accurate estimates of ground water, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited.. The new algorithm enhances the temporal resolution of high spatial resolution of soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research