stats (version 3.3.2)

HoltWinters: Holt-Winters Filtering

Description

Computes Holt-Winters Filtering of a given time series. Unknown parameters are determined by minimizing the squared prediction error.

Usage

HoltWinters(x, alpha = NULL, beta = NULL, gamma = NULL,
            seasonal = c("additive", "multiplicative"),
            start.periods = 2, l.start = NULL, b.start = NULL,
            s.start = NULL,
            optim.start = c(alpha = 0.3, beta = 0.1, gamma = 0.1),
            optim.control = list())

Arguments

x

An object of class ts

alpha

\(alpha\) parameter of Holt-Winters Filter.

beta

\(beta\) parameter of Holt-Winters Filter. If set to FALSE, the function will do exponential smoothing.

gamma

\(gamma\) parameter used for the seasonal component. If set to FALSE, an non-seasonal model is fitted.

seasonal

Character string to select an "additive" (the default) or "multiplicative" seasonal model. The first few characters are sufficient. (Only takes effect if gamma is non-zero).

start.periods

Start periods used in the autodetection of start values. Must be at least 2.

l.start

Start value for level (a[0]).

b.start

Start value for trend (b[0]).

s.start

Vector of start values for the seasonal component (\(s_1[0] \ldots s_p[0]\))

optim.start

Vector with named components alpha, beta, and gamma containing the starting values for the optimizer. Only the values needed must be specified. Ignored in the one-parameter case.

optim.control

Optional list with additional control parameters passed to optim if this is used. Ignored in the one-parameter case.

Value

An object of class "HoltWinters", a list with components:

fitted

A multiple time series with one column for the filtered series as well as for the level, trend and seasonal components, estimated contemporaneously (that is at time t and not at the end of the series).

x

The original series

alpha

alpha used for filtering

beta

beta used for filtering

gamma

gamma used for filtering

coefficients

A vector with named components a, b, s1, ..., sp containing the estimated values for the level, trend and seasonal components

seasonal

The specified seasonal parameter

SSE

The final sum of squared errors achieved in optimizing

call

The call used

Details

The additive Holt-Winters prediction function (for time series with period length p) is $$\hat Y[t+h] = a[t] + h b[t] + s[t - p + 1 + (h - 1) \bmod p],$$ where \(a[t]\), \(b[t]\) and \(s[t]\) are given by $$a[t] = \alpha (Y[t] - s[t-p]) + (1-\alpha) (a[t-1] + b[t-1])$$ $$b[t] = \beta (a[t] -a[t-1]) + (1-\beta) b[t-1]$$ $$s[t] = \gamma (Y[t] - a[t]) + (1-\gamma) s[t-p]$$ The multiplicative Holt-Winters prediction function (for time series with period length p) is $$\hat Y[t+h] = (a[t] + h b[t]) \times s[t - p + 1 + (h - 1) \bmod p].$$ where \(a[t]\), \(b[t]\) and \(s[t]\) are given by $$a[t] = \alpha (Y[t] / s[t-p]) + (1-\alpha) (a[t-1] + b[t-1])$$ $$b[t] = \beta (a[t] - a[t-1]) + (1-\beta) b[t-1]$$ $$s[t] = \gamma (Y[t] / a[t]) + (1-\gamma) s[t-p]$$ The data in x are required to be non-zero for a multiplicative model, but it makes most sense if they are all positive. The function tries to find the optimal values of \(\alpha\) and/or \(\beta\) and/or \(\gamma\) by minimizing the squared one-step prediction error if they are NULL (the default). optimize will be used for the single-parameter case, and optim otherwise. For seasonal models, start values for a, b and s are inferred by performing a simple decomposition in trend and seasonal component using moving averages (see function decompose) on the start.periods first periods (a simple linear regression on the trend component is used for starting level and trend). For level/trend-models (no seasonal component), start values for a and b are x[2] and x[2] - x[1], respectively. For level-only models (ordinary exponential smoothing), the start value for a is x[1].

References

C. C. Holt (1957) Forecasting trends and seasonals by exponentially weighted moving averages, ONR Research Memorandum, Carnegie Institute of Technology 52. P. R. Winters (1960) Forecasting sales by exponentially weighted moving averages, Management Science 6, 324--342.

See Also

predict.HoltWinters, optim.

Examples

require(graphics)

## Seasonal Holt-Winters
(m <- HoltWinters(co2))
plot(m)
plot(fitted(m))

(m <- HoltWinters(AirPassengers, seasonal = "mult"))
plot(m)

## Non-Seasonal Holt-Winters
x <- uspop + rnorm(uspop, sd = 5)
m <- HoltWinters(x, gamma = FALSE)
plot(m)

## Exponential Smoothing
m2 <- HoltWinters(x, gamma = FALSE, beta = FALSE)
lines(fitted(m2)[,1], col = 3)