COMPARATIVE EVALUATION OF FORECAST ACCURACIES FOR ARIMA, EXPONENTIAL SMOOTHING AND VAR

Prediction of global sea cucumber capture production based on the exponential smoothing and ARIMA models

Sea cucumber catch has followed “boom-and-bust” patterns over the period of 60 years from 1950-2010, and sea cucumber fisheries have had important ecological, economic and societal roles. However, sea cucumber fisheries have not been explored systematically, especially in terms of catch change trends. Sea cucumbers are relatively sedentary species. An attempt was made to explore whether the tim...

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

Prediction of global sea cucumber capture production based on the exponential smoothing and ARIMA models

Sea cucumber catch has followed “boom-and-bust” patterns over the period of 60 years from 1950-2010, and sea cucumber fisheries have had important ecological, economic and societal roles. However, sea cucumber fisheries have not been explored systematically, especially in terms of catch change trends. Sea cucumbers are relatively sedentary species. An attempt was made to explore whe...

a comparison between time series, exponential smoothing, and neural network methods to forecast gdp of iran

Bayesian forecast combination for VAR models∗

This paper proposes a Bayesian procedure for combining forecasts from multivariate forecasting models, e.g. VAR models. Standard applications of Bayesian model averaging suffer from a basic difficulty in this context, when additional variables are included and modelled the connection between the overall measure of fit for the model and the expected forecasting performance for the variables of i...