| Package | Description |
|---|---|
| dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime | |
| dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime | |
| dev.nm.stat.timeseries.linear.univariate.arima | |
| dev.nm.stat.timeseries.linear.univariate.sample | |
| dev.nm.stat.timeseries.linear.univariate.stationaryprocess | |
| dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma |
ARMA model fitting and forecasting from an ordered observation history.
|
| Constructor and Description |
|---|
MultivariateSimpleTimeSeries(IntTimeTimeSeries ts)
Construct an instance of
MultivariateSimpleTimeSeries from a univariate time series. |
| Modifier and Type | Class and Description |
|---|---|
class |
DifferencedIntTimeTimeSeries
Differencing of a time series \(x_{t}\) in discrete time t is the
transformation of the series to a new time series \((1-B)x_{t}\) where the new values
are the differences between consecutive values of \(x_{t}\).
|
class |
SimpleTimeSeries
This simple univariate time series simply wraps a
double[] to form a time series. |
| Constructor and Description |
|---|
DifferencedIntTimeTimeSeries(IntTimeTimeSeries xt,
int d) |
| Constructor and Description |
|---|
ARIMAForecast(IntTimeTimeSeries xt,
ARIMAModel arima)
Constructs a forecaster for a time series assuming ARIMA model.
|
ARIMAForecast(IntTimeTimeSeries xt,
int p,
int d,
int q,
double epsilon)
Constructs a forecaster for a time series assuming ARIMA model.
|
ARIMAForecastMultiStep(IntTimeTimeSeries xt,
ARIMAModel arima,
int h)
Makes the h-step ahead prediction for an ARIMA model.
|
| Constructor and Description |
|---|
SampleAutoCorrelation(IntTimeTimeSeries xt)
Construct the sample ACF for a time series.
|
SampleAutoCorrelation(IntTimeTimeSeries xt,
SampleAutoCovariance.Type type)
Construct the sample ACF for a time series.
|
SampleAutoCovariance(IntTimeTimeSeries xt)
Construct the sample ACVF for a time series.
|
SampleAutoCovariance(IntTimeTimeSeries xt,
SampleAutoCovariance.Type type)
Construct the sample ACVF for a time series.
|
SamplePartialAutoCorrelation(IntTimeTimeSeries xt)
Construct the sample PACF for a time series.
|
SamplePartialAutoCorrelation(IntTimeTimeSeries xt,
SampleAutoCovariance.Type type)
Construct the sample PACF for a time series.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AdditiveModel
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
\[
Y[t] = T[t] + S[t] + e[t]
\]
|
class |
MultiplicativeModel
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
\[
Y[t] = T[t] \cdot S[t] \cdot e[t]
\]
|
| Constructor and Description |
|---|
ARMAForecast(IntTimeTimeSeries xt,
ARMAModel arma)
Constructs a forecaster for a time series assuming ARMA model.
|
ARMAForecast(IntTimeTimeSeries xt,
int p,
int q)
Constructs a forecaster for a time series assuming ARMA model.
|
ARMAForecastMultiStep(IntTimeTimeSeries xt,
ARMAModel arma)
Makes the one-step ahead prediction for an ARMA model.
|
ARMAForecastMultiStep(IntTimeTimeSeries xt,
ARMAModel arma,
int h)
Makes the h-step ahead prediction for an ARMA model.
|
ARMAForecastMultiStep(IntTimeTimeSeries xt,
ARMAModel arma,
int h,
InnovationsAlgorithm inn)
Makes the h-step ahead prediction for an ARMA model.
|
ARMAForecastMultiStep(IntTimeTimeSeries xt,
ARMAModel arma,
int h,
InnovationsAlgorithm inn,
ARMAForecastOneStep forecast1)
Makes the h-step ahead prediction for an ARMA model.
|
ARMAForecastOneStep(IntTimeTimeSeries xt,
ARMAModel arma)
Makes the one-step ahead prediction for an ARMA model.
|
ARMAForecastOneStep(IntTimeTimeSeries xt,
ARMAModel arma,
InnovationsAlgorithm inn)
Makes the one-step ahead prediction for an ARMA model.
|
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