com.github.signaflo.timeseries.model

Class RandomWalk

java.lang.Object

com.github.signaflo.timeseries.model.RandomWalk

All Implemented Interfaces:

Model

public final class RandomWalk
extends Object
implements Model

A model for a random walk process. Some important characteristics of the random walk are that the process variance increases with time (non-stationarity) and that the optimal forecast at any point in the future is equal to the last observed value.

Constructor Summary

Constructors

Constructor and Description
RandomWalk(TimeSeries observed) Create a new random walk model from the given time series of observations.

Method Summary

Modifier and Type	Method and Description
boolean	equals(Object o)
TimeSeries	fittedSeries() Get the model fitted values, which are in-sample one-step ahead forecasts.
Forecast	forecast(int steps, double alpha) Produce a forecast from this model up to the given number of steps ahead.
int	hashCode()
TimeSeries	observations() Get the series of observations.
TimeSeries	predictionErrors() Get the model prediction errors, the difference between the observed values and the model fitted values.
static TimeSeries	simulate(Distribution dist, int n) Simulate a random walk assuming that the errors, or random shocks, follow the given Distribution.
static TimeSeries	simulate(double mean, double sigma, int n) Simulate a random walk assuming errors follow a Normal (Gaussian) Distribution with the given mean and standard deviation.
static TimeSeries	simulate(double sigma, int n) Simulate a random walk assuming errors follow a Normal (Gaussian) Distribution with zero mean and with the provided standard deviation.
static TimeSeries	simulate(int n) Simulate a random walk assuming errors follow a standard Normal (Gaussian) Distribution.
String	toString()

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.github.signaflo.timeseries.model.Model

forecast

Constructor Detail

RandomWalk

public RandomWalk(@NonNull
                  TimeSeries observed)

Create a new random walk model from the given time series of observations.

Parameters:

observed - the observed series.

Method Detail

simulate

public static TimeSeries simulate(@NonNull
                                  Distribution dist,
                                  int n)

Simulate a random walk assuming that the errors, or random shocks, follow the given Distribution.

Parameters:

dist - The probability distribution that observations are drawn from.

n - The number of observations to simulate.

Returns:

the simulated series.

simulate

public static TimeSeries simulate(double mean,
                                  double sigma,
                                  int n)

Simulate a random walk assuming errors follow a Normal (Gaussian) Distribution with the given mean and standard deviation.

Parameters:

mean - the mean of the Normal distribution the observations are drawn from.

sigma - the standard deviation of the Normal distribution the observations are drawn from.

n - the number of observations to simulate.

Returns:

the simulated series.

simulate

public static TimeSeries simulate(double sigma,
                                  int n)

Simulate a random walk assuming errors follow a Normal (Gaussian) Distribution with zero mean and with the provided standard deviation.

Parameters:

sigma - the standard deviation of the Normal distribution the observations are drawn from.

n - the number of observations to simulate.

Returns:

the simulated series.

simulate

public static TimeSeries simulate(int n)

Simulate a random walk assuming errors follow a standard Normal (Gaussian) Distribution.

Parameters:

n - the number of observations to simulate.

Returns:

the simulated series.

forecast

public Forecast forecast(int steps,
                         double alpha)

Description copied from interface: Model

Produce a forecast from this model up to the given number of steps ahead.

Specified by:

forecast in interface Model

Parameters:

steps - the number of time periods ahead to forecast.

alpha - the significance level to use for the prediction interval.

Returns:

a forecast from this model up to the given number of steps ahead.

observations

public TimeSeries observations()

Description copied from interface: Model

Get the series of observations.

Specified by:

observations in interface Model

Returns:

the series of observations.

fittedSeries

public TimeSeries fittedSeries()

Description copied from interface: Model

Get the model fitted values, which are in-sample one-step ahead forecasts.

Specified by:

fittedSeries in interface Model

Returns:

the model fitted values.

predictionErrors

public TimeSeries predictionErrors()

Description copied from interface: Model

Get the model prediction errors, the difference between the observed values and the model fitted values.

Specified by:

predictionErrors in interface Model

Returns:

the model prediction errors.

toString

public String toString()

Overrides:

toString in class Object

equals

public boolean equals(Object o)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object