ma.filter: Refined Moving Average Filter
In debinqiu/rmaf: Refined Moving Average Filter

Description Usage Arguments Details Value Author(s) References See Also Examples

uses refined moving average filter to estimate the trend component, and then obtain seasonal component if necessary.

1	ma.filter(x, q = NULL, seasonal = FALSE, period = NULL, plot = TRUE)

`x`	a numeric vector or univariate time series.
`seasonal`	a logical value indicating to estimate the seasonal component. Only valid for `seasonal = TRUE`. The default is `FALSE`.
`q`	specified moving average lag q. The default is `NULL`.
`period`	seasonal period. The default is `NULL`.
`plot`	a logical value indicating to make the plots. The default is `TRUE`.

For univariate time seties x[t], the additive seasonal model is assumed to be

x[t] = m[t] + S[t] + R[t],

where m[t], S[t], R[t] are trend, seasonal and irregular components, respectively. Once we obtain the optimal moving average lag q using qn, the trend can be estimated by using the refined moving average

mhat[t] = ∑ x[t]/(2q+1),

for q + 1 ≤ t ≤ n - q. If q + 1 > n - q, we take q = min(n - q, q). If there is no seasonal component, the irregularity or residuals can be computed by Rhat[t] = x[t] - mhat[t]. Otherwise, the seasonal index Shat[t] can be estimated by averaging the sequence x[t] - mhat[t] for each of 1:period. For example, the seasonal component in January can be estimated by the average of all of the observations made in January after removing the trend component. To ensure the identifiability of m[t] and S[t], we have to assume

S[i + j*period] = S[i], ∑ S[i] = 0,

where i = 1,..., period; j = floor(n/period). The irregularity or residuals are then computed by Rhat[t] = x[t] - mhat[t] - Shat[t]. For t < q + 1 and t > n - q, the corresponding estimators are based on equation (7) in D. Qiu et al. (2013). More details about estimating the trend component can be seen in Section 1.5 of P.J. Brockwell et al. (1991) or Chapter 6 of J. Fan et al. (2003).

For the multiplicative seasonal model

x[t] = m[t] * S[t] * R[t],

it can be transformed to an additive seasonal model by taking a logarithm on both sides if x[t] > 0, i.e.,

log(x[t]) = log(m[t]) + log(S[t]) + log(R[t]),

and then use the refined moving average filter for the components decomposition as the same in the additive seasonal model.

Plots of original data v.s fitted data, fitted trend, seasonal indices (if seasonal = TRUE) and residuals will be drawn if plot = TRUE.

A matrix containing the following columns:

`data`	original data `x`.
`trend`	fitted trend.
`season`	seasonal indices if `seasonal = TRUE`.
`residual`	irregularity or residuals.

Debin Qiu

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.

ss.filter

## decompose the trend for the first difference of annual global air temperature from 1880-1985
data(globtemp)
decomp1 <- ma.filter(globtemp)

## decompose the trend and seasonality for CO2 data with monthly and additive seasonality
decomp2 <- ma.filter(co2, seasonal = TRUE, period = 12)

## decompose the trend and seasonality for monthly airline passenger numbers from 1949-1960
decomp3 <- ma.filter(AirPassengers, seasonal = TRUE, period = 12)

## simulation data: oracally efficient estimation for AR(p) coefficients
d <- 12
n <- d*100
x <- (1:n)/n
y <- 1 + 2*x + 0.3*x^2 + sin(pi*x/6) + arima.sim(n = n,list(ar = 0.2), sd = 1)
fit <- ma.filter(y,seasonal = TRUE,period = 12,plot = FALSE)
ar(fit[,4], aic = FALSE, order.max = 1)$ar