ARMA

An auto-regressive, moving-average model.

It provides a weakly stationary stochastic process as a sum of two polynomials - one for the auto-regression - one for the moving average

The time series is built as follows:

X_t = c + epsilon_t + ∑(phi_i * X_(t-1)) + ∑(theta_i * epsilon_(t-1))

Where epsilon is generated from a white noise of a specified standard deviation.

phi: the parameters used to characterize the autoregression part of the model
theta: the parameters used to characterize the moving average part of the model
std: the standard deviation used to characterize the generated white noise
c: a constant
seed: the seed used to generate the white noise. For a given seed, the process is deterministic

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

new ARMA(phi: Array[Double] = Array(), theta: Array[Double] = Array(), std: Double = 1, c: Double = 0, seed: Long = Random.nextLong)

phi
the parameters used to characterize the autoregression part of the model
theta
the parameters used to characterize the moving average part of the model
std
the standard deviation used to characterize the generated white noise
c
a constant
seed
the seed used to generate the white noise. For a given seed, the process is deterministic

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val c: Double

a constant
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val phi: Array[Double]

the parameters used to characterize the autoregression part of the model
val seed: Long

the seed used to generate the white noise.
the seed used to generate the white noise. For a given seed, the process is deterministic
def series: Stream[Double]

Generates a sequence of values using a Random path progress, and based on the specified ARMA parameters.
Generates a sequence of values using a Random path progress, and based on the specified ARMA parameters.
returns
A sequence of values representing a discrete time series.
val std: Double

the standard deviation used to characterize the generated white noise
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val theta: Array[Double]

the parameters used to characterize the moving average part of the model
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package primary

case class ARMA(phi: Array[Double] = Array(), theta: Array[Double] = Array(), std: Double = 1, c: Double = 0, seed: Long = Random.nextLong) extends Product with Serializable

Instance Constructors

new ARMA(phi: Array[Double] = Array(), theta: Array[Double] = Array(), std: Double = 1, c: Double = 0, seed: Long = Random.nextLong)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val c: Double

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val phi: Array[Double]

val seed: Long

def series: Stream[Double]

val std: Double

final def synchronized[T0](arg0: ⇒ T0): T0

val theta: Array[Double]

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped