smile.timeseries

Interface TimeSeries

public interface TimeSeries

Time series utility functions.

Method Summary

Modifier and Type	Method and Description
static double	acf(double[] x, int lag) Autocorrelation function.
static double	cov(double[] x, int lag) Autocovariance function.
static double[]	diff(double[] x, int lag) Returns the first-differencing of time series.
static double[][]	diff(double[] x, int lag, int differences) Returns the differencing of time series.
static double	pacf(double[] x, int lag) Partial autocorrelation function.

Method Detail

diff

static double[] diff(double[] x,
                     int lag)

Returns the first-differencing of time series. First-differencing a time series will remove a linear trend (i.e., differences=1). In addition, first-differencing a time series at a lag equal to the period will remove a seasonal trend (e.g., set lag=12 for monthly data).

Parameters:

x - time series

lag - the lag at which to difference

diff

static double[][] diff(double[] x,
                       int lag,
                       int differences)

Returns the differencing of time series. First-differencing a time series will remove a linear trend (i.e., differences=1); twice-differencing will remove a quadratic trend (i.e., differences=2). In addition, first-differencing a time series at a lag equal to the period will remove a seasonal trend (e.g., set lag=12 for monthly data).

Parameters:

x - time series

lag - the lag at which to difference

differences - the order of differencing

cov

static double cov(double[] x,
                  int lag)

Autocovariance function.

Parameters:

x - time series

lag - the lag

acf

static double acf(double[] x,
                  int lag)

Autocorrelation function.

Parameters:

x - time series

lag - the lag

pacf

static double pacf(double[] x,
                   int lag)

Partial autocorrelation function. The partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, regressed the values of the time series at all shorter lags.

Parameters:

x - time series

lag - the lag