Volume 17, Issue 40 (Mathematic- 2015)
2015, 17(40): 1-12 |
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Abstract: (6022 Views)
One of the main goals in 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 a autoregressive process. Then the bootstrap samples are generated by resampling from the residuals.
In this paper, at first these sieve bootstrap methods are defined and then, in a simulation study sieve bootstrap prediction intervals are compared with a Standard Gaussian prediction interval. at last these methods are used to find the prediction intervals for weather data of Isfahan.
One of the main goals in 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 a autoregressive process. Then the bootstrap samples are generated by resampling from the residuals.
In this paper, at first these sieve bootstrap methods are defined and then, in a simulation study sieve bootstrap prediction intervals are compared with a Standard Gaussian prediction interval. at last these methods are used to find the prediction intervals for weather data of Isfahan.
In this paper, at first these sieve bootstrap methods are defined and then, in a simulation study sieve bootstrap prediction intervals are compared with a Standard Gaussian prediction interval. at last these methods are used to find the prediction intervals for weather data of Isfahan.
One of the main goals in 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 a autoregressive process. Then the bootstrap samples are generated by resampling from the residuals.
In this paper, at first these sieve bootstrap methods are defined and then, in a simulation study sieve bootstrap prediction intervals are compared with a Standard Gaussian prediction interval. at last these methods are used to find the prediction intervals for weather data of Isfahan.
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