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

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

Forecasting Compositional Time Series with Exponential Smoothing Methods

Compositional time series are formed from measurements of proportions that sum to one in each period of time. We might be interested in forecasting the proportion of home loans that have adjustable rates, the proportion of nonagricultural jobs in manufacturing, the proportion of a specific oxide in the geochemical composition of a rock, or the proportion of an election betting market choosing a...

A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine

Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP.

a comparison of teachers and supervisors, with respect to teacher efficacy and reflection

supervisors play an undeniable role in training teachers, before starting their professional experience by preparing them, at the initial years of their teaching by checking their work within the proper framework, and later on during their teaching by assessing their progress. but surprisingly, exploring their attributes, professional demands, and qualifications has remained a neglected theme i...

Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication

In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a “synergetic” model via the linear programming technique. For verification, two real-world financial...

Using Wavelets and Splines to Forecast Non-Stationary Time Series

 This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...