rmaf (version 3.0.1)

rmaf-package: Refined Moving Average Filter Package

Description

A refined moving average filter using the optimal and data-driven moving average lag $q$ to estimate the trend component, and then estimate seasonal component and irregularity for univariate time series or data.

Details

Package:

rmaf

Type:

Package

Version:

3.0.1

Date:

2015-04-14

License:

GPL (>= 2)

This package contains a function to determine the optimal and data-driven moving average lag $q$, and two functions to estimate the trend, seasonal component and irregularity for univariate time series. A dataset of the first differences of annual global surface air temperatures in Celsius from 1880 through 1985 is also included in the package for illustrating the trend estimation.

For a complete list of functions and dataset, use library(help = rmaf).

References

D. Qiu, Q. Shao, and L. Yang (2013), Efficient inference for autoregressive coeficient in the presence of trend. Journal of Multivariate Analysis 114, 40-53.

J. Fan and Q. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods, first ed., Springer, New York, 2003.

P.J. Brockwell, R.A. Davis, Time Series: Theory and Methods, second ed., Springer, New York, 1991.

See Also

ma.filter, ss.filter, qn

Examples

## The first difference of annual global surface air temperatures from 1880 to 1985 with only trend
data(globtemp)
q.n <- qn(globtemp)
fit1 <- ma.filter(globtemp)
fit2 <- ss.filter(globtemp)