# Interface TimeSeries

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

Static Methods
Modifier and Type
Method
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 Details

• ### 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
Returns:
the first-differencing of time series.
• ### 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
Returns:
the differencing of time series.
• ### cov

static double cov(double[] x, int lag)
Autocovariance function.
Parameters:
`x` - time series.
`lag` - the lag.
Returns:
autocovariance.
• ### acf

static double acf(double[] x, int lag)
Autocorrelation function.
Parameters:
`x` - time series.
`lag` - the lag.
Returns:
autocorrelation.
• ### 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.
Returns:
partial autocorrelation.