class ARMA extends Predictor with Error
The ARMA
class provide basic time series analysis capabilities for Auto-
Regressive 'AR' and Moving Average 'MA' models. In an 'ARMA(p, q)' model,
'p' and 'q' refer to the order of the Auto-Regressive and Moving Average
components of the model. ARMA
models are often used for forecasting.
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def
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def
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- val acf: VectorD
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def
ar(phi: VectorD): VectorD
Return a vector that is the predictions of a 'p'th order Auto-Regressive 'AR(p)) model.
Return a vector that is the predictions of a 'p'th order Auto-Regressive 'AR(p)) model. 'f_t = phi_0 * x_t-1 + ... + phi_p-1 * x_t-p + e_t'
- phi
the estimated 'AR(p)' coefficients
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val
b: VectoD
Coefficient/parameter vector [b_0, b_1, ...
Coefficient/parameter vector [b_0, b_1, ... b_k]
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
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def
durbinLevinson: MatrixD
Apply the Durbin-Levinson Algorithm to iteratively compute the 'psi' matrix.
Apply the Durbin-Levinson Algorithm to iteratively compute the 'psi' matrix. The last row of the matrix gives 'AR' coefficients.
- See also
www.stat.tamu.edu/~suhasini/teaching673/time_series.pdf
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val
e: VectoD
Residual/error vector [e_0, e_1, ...
Residual/error vector [e_0, e_1, ... e_m-1]
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def
est_ar(p: Int = 1): VectorD
Estimate the coefficients for a 'p'th order Auto-Regressive 'AR(p)' model.
Estimate the coefficients for a 'p'th order Auto-Regressive 'AR(p)' model. 'x_t = phi_0 * x_t-1 + ... + phi_p-1 * x_t-p + e_t' Uses the Durbin-Levinson Algorithm to determine the coefficients. The 'phi' vector is 'p'th row of 'psi' matrix (ignoring the first (0th) column).
- p
the order of the 'AR' model
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def
est_ma(q: Int = 1): VectorD
Estimate the coefficients for a 'q'th order a Moving Average(q) model.
Estimate the coefficients for a 'q'th order a Moving Average(q) model. x_t = e_t - theta_0 * e_t-1 - ... - theta_q-1 * e_t-q FIX: use Method of Innovations
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finalize(): Unit
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def
fit: VectorD
Return the quality of fit including 'rSquared'.
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def
fitLabels: Array[String]
Return the labels for the fit.
Return the labels for the fit. Override when necessary.
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final
def
flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
- method
the method where the error occurred
- message
the error message
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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isInstanceOf[T0]: Boolean
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def
ma(theta: VectorD): VectorD
Return a vector that is the predictions of a 'q'th order Moving Average (MA) model.
Return a vector that is the predictions of a 'q'th order Moving Average (MA) model. x_t = e_t + theta_0 * e_t-1 + ... + theta_q-1 * e_t-q
- theta
the estimated MA(q) coefficients
- val mu: Double
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def
notify(): Unit
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def
notifyAll(): Unit
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- var pacf: VectorD
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def
plotFunc(fVec: VectorD, name: String): Unit
Plot a function, e.g., Auto-Correlation Function 'ACF', Partial Auto-Correlation Function 'PACF'.
Plot a function, e.g., Auto-Correlation Function 'ACF', Partial Auto-Correlation Function 'PACF'.
- fVec
the vector given function values
- name
the name of the function
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def
predict(y: VectoD): Double
For all the time points in vector t, predict the value of y = f(t) by ...
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def
predict(z: VectorI): Double
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
- z
the vector to use for prediction
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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- val sig2: Double
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def
smooth(l: Int): VectorD
Smooth the 'y' vector by taking the 'l'th order moving average.
Smooth the 'y' vector by taking the 'l'th order moving average.
- l
the number of points to average
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(): Unit
Fit an
ARMA
model to times series data. -
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def
wait(): Unit
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