Add to ORKGThis paper focuses on developing a new data-driven procedure for decomposing seasonal time series based on local regression. Formula of the asymptotic optimal bandwidth hA in the current context is given. Methods for estimating the unknowns in hA are investigated. A data-driven algorithm for decomposing seasonal time series is proposed based on the iterative plug-in idea introduced by Gasser et al. (1991). Asymptotic behaviour of this algorithm is investigated. Some computational aspects are discussed in detail. Practical performance of the proposed algorithm is illustrated by simulated and data examples. The results here also provide some insights into the iterative plug-in idea
In the first chapter of this dissertation, I briefly introduce one type of nonparametric regression ...
Abstract. As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorith...
Decomposing complex time series into trend, seasonality, and remainder components is an important ta...
This paper focuses on developing a new data-driven procedure for decomposing seasonal time series ba...
This paper focuses on developing a new data-driven procedure for decomposing seasonal time series ba...
We propose a fast data-driven procedure for decomposing seasonal time series using the Berlin Method...
We present a nonparametric method to forecast a seasonal univariate time series, and propose four dy...
Abstract: We present a nonparametric method to forecast a seasonal time series, and propose four dyn...
A time series often contains various systematic effects such as trends and seasonality. These differ...
New innovations state space modeling tools, incorporating Box-Cox transformations, Fourier series wi...
The Spanish industrial production index, like similar indexes for other countries, contains a mixtur...
Seasonality (or periodicity) and trend are features describing an observed sequence, and extracting ...
We present a method for investigating the evolution of trend and seasonality in an observed time ser...
In this paper a robust data-driven procedure for decomposing seasonal time series based on a general...
This work analyzes the estimation of a time varying coefficients regression model in presence of sea...
In the first chapter of this dissertation, I briefly introduce one type of nonparametric regression ...
Abstract. As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorith...
Decomposing complex time series into trend, seasonality, and remainder components is an important ta...
This paper focuses on developing a new data-driven procedure for decomposing seasonal time series ba...
This paper focuses on developing a new data-driven procedure for decomposing seasonal time series ba...
We propose a fast data-driven procedure for decomposing seasonal time series using the Berlin Method...
We present a nonparametric method to forecast a seasonal univariate time series, and propose four dy...
Abstract: We present a nonparametric method to forecast a seasonal time series, and propose four dyn...
A time series often contains various systematic effects such as trends and seasonality. These differ...
New innovations state space modeling tools, incorporating Box-Cox transformations, Fourier series wi...
The Spanish industrial production index, like similar indexes for other countries, contains a mixtur...
Seasonality (or periodicity) and trend are features describing an observed sequence, and extracting ...
We present a method for investigating the evolution of trend and seasonality in an observed time ser...
In this paper a robust data-driven procedure for decomposing seasonal time series based on a general...
This work analyzes the estimation of a time varying coefficients regression model in presence of sea...
In the first chapter of this dissertation, I briefly introduce one type of nonparametric regression ...
Abstract. As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorith...
Decomposing complex time series into trend, seasonality, and remainder components is an important ta...