PREDICTION METHODS FOR 1/f PROCESSES WITH APPLICATION TO THE ANALYSIS OF STRIDE INTERVAL TIME SERIES by

Some New Methods for Prediction of Time Series by Wavelets

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

AN ASSESSMENT OF FRACTAL CHARACTERIZATION METHODS FOR 1/f PROCESSES WITH APPLICATION TO THE ANALYSIS OF STRIDE INTERVAL TIME SERIES by

Functional-Coefficient Autoregressive Model and its Application for Prediction of the Iranian Heavy Crude Oil Price

Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...

Semiparametric Bootstrap Prediction Intervals in time Series

One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...

River Discharge Time Series Prediction by Chaos Theory

The application of chaos theory in hydrology has been gaining considerable interest in recent years.Based on the chaos theory, the random seemingly series can be attributed to deterministic rules. Thedynamic structures of the seemingly complex processes, such as river flow variations, might be betterunderstood using nonlinear deterministic chaotic models than the stochastic ones. In this paper,...