Mehdi Khashei , Farimah Mokhatab Rafiei, Mehdi Bijari ,
Volume 23, Issue 4 (11-2012)
Abstract
In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficient once in financial markets. In this paper, the performance of four interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressive integrated moving average (FARIMA), hybrid ANNs and fuzzy (FANN) and Improved FARIMA models are compared together. Empirical results of exchange rate forecasting indicate that the FANN model is more satisfactory than other those models. Therefore, it can be a suitable alternative model for interval forecasting of financial time series.
Dr Chinedum Mgbemena, Dr Emmanuel Chinwuko,
Volume 31, Issue 1 (3-2020)
Abstract
Crude oil production output forecast is very important in the formulation of genuine and suitable production policies; it is pivotal in planning and decision making. This paper explores the use of forecasting techniques to assist the oil field manager in decision making. In this analysis, statistical models of projected trends which involves graphical, least squares, simple moving average and exponential smoothing methods were compared. The least squares method was found to be most suitable to capture the recent random nature of crude oil production output in the oilfield of the Niger Delta region of Nigeria. In addition, a multiple linear regression model was developed for predicting daily, weekly, monthly or even yearly volume of crude oil production output in the oilfield facility.
