smile.timeseries

Interface TimeSeries

• public interface TimeSeries
Time series utility functions.
• Method Summary

All Methods
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