CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

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

Exponential Smoothing, Long Memory and Volatility Prediction

Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and signal extraction scheme, but it can be suboptimal for long memory time series. This paper discusses po...

Damped Trend Exponential Smoothing: Prediction and Control

Damped trend exponential smoothing models have gained importance in empirical studies due to their remarkable forecasting performance. This paper derives their theoretical forecast error variance, based on the implied ARIMA model, as algebraic function of the structural parameters. As a consequence, the minimum mean squared error (MMSE) forecasts as well as the h-step ahead theoretical forecast...

Exponential Smoothing: A Prediction Error Decomposition Principle

In the exponential smoothing approach to forecasting, restrictions are often imposed on the smoothing parameters which ensure that certain components are exponentially weighted averages. In this paper, a new general restriction is derived on the basis that the one-step ahead prediction error can be decomposed into permanent and transient components. It is found that this general restriction red...