Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models

The Comparison among ARIMA and hybrid ARIMA-GARCH Models in Forecasting the Exchange Rate of Iran

This paper attempts to compare the forecasting performance of the ARIMA model and hybrid ARMA-GARCH Models by using daily data of the Iran’s exchange rate against the U.S. Dollar (IRR/USD) for the period of 20 March 2014 to 20 June 2015. The period of 20 March 2014 to 19 April 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasti...

the comparison among arima and hybrid arima-garch models in forecasting the exchange rate of iran

this paper attempts to compare the forecasting performance of the arima model and hybrid arma-garch models by using daily data of the iran’s exchange rate against the u.s. dollar (irr/usd) for the period of 20 march 2014 to 20 june 2015. the period of 20 march 2014 to 19 april 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasti...

Forecasting irish inflation using ARIMA models

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models the Box Jenkins approach and the objective penalty function methods. The emphasis is on forec...

Small-sample Forecasting Regression or Arima Models?

TES Processes and ARIMA Models: A Comparison of Forecasting Performance

Forecasting is of prime importance for accuracy in decision-making. For data sets containing high autocorrelations, failure to account for temporal dependence will result in poor forecasting. TES (Transform-Expand-Sample) is a class of stochastic processes to model empirical autocorrelated time series and is frequently used in Monte Carlo simulation. Its merit is to capture simultaneously both ...