rmaf-package: Refined Moving Average Filter Package
In rmaf: Refined Moving Average Filter

Description Details Author(s) References See Also Examples

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.

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).

Debin Qiu

Maintainer: Debin Qiu <debinqiu@uga.edu>

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.

ma.filter, ss.filter, qn

## 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)