class ExpSmoothing extends Predictor with Error
The ExpSmoothing class provide very basic time series analysis capabilities of
Exponential Smoothing models. ExpSmoothing models are often used for forecasting.
Given time series data stored in vector 'y', its next value 'y_t = y(t)'
may be predicted based on prior/smoothed values of 'y':
y_t = s_t-1 + α (s_t-1 - s_t-2)
where vector 's' is the smoothed version of vector 'y' and 'α in [0, 1]' is the smoothing parameter. ------------------------------------------------------------------------------
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new
ExpSmoothing(y: VectoD, t: VectoD)
- y
the input vector (time series data)
- t
the time vector
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
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def
clone(): AnyRef
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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
diagnose(yy: VectoD): Unit
Compute diagostics for the Exponential Smoothing model.
Compute diagostics for the Exponential Smoothing model.
- yy
the response vector
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- ExpSmoothing → Predictor
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val
e: VectoD
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final
def
eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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def
f_obj(αα: Double): Double
The objective function to be minimized (sum of squared errors for the given 'αα').
The objective function to be minimized (sum of squared errors for the given 'αα').
- αα
the parameter of the objective function to be optimized
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def
finalize(): Unit
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def
fit: VectorD
Return the quality of fit including 'rSquared'.
Return the quality of fit including 'rSquared'. Not providing 'rBarSq'.
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- ExpSmoothing → Predictor
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def
fitLabels: Seq[String]
Return the labels for the fit.
Return the labels for the fit.
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final
def
flaw(method: String, message: String): Unit
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def
getClass(): Class[_]
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hashCode(): Int
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final
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isInstanceOf[T0]: Boolean
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val
mae: Double
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ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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def
notifyAll(): Unit
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def
predict(s: VectoD): Double
Predict the next (unknown) value in the time-series.
Predict the next (unknown) value in the time-series.
- s
the smoothed time-series data
- Definition Classes
- ExpSmoothing → Predictor
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def
predict(z: VectoI): 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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val
rSq: Double
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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val
rmse: Double
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def
smooth(α_new: Double = α): VectorD
Smooth the times series data.
Smooth the times series data.
- α_new
the new smoothing parameter, skip to use default
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val
sse: Double
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val
ssr: Double
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val
sst: Double
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(): Unit
Train the
ExpSmoothingmodel to times series data, by finding the value for the smoothing parameter 'α' that minimizes the sum of squared errors (sse).Train the
ExpSmoothingmodel to times series data, by finding the value for the smoothing parameter 'α' that minimizes the sum of squared errors (sse). Use the response passed into the class 'y'.- Definition Classes
- ExpSmoothing → Predictor
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def
train(yy: VectoD): Unit
Train the
ExpSmoothingmodel to times series data, by finding the value for the smoothing parameter 'α' that minimizes the sum of squared errors (sse).Train the
ExpSmoothingmodel to times series data, by finding the value for the smoothing parameter 'α' that minimizes the sum of squared errors (sse). FIX - use either a penalty or cross-validation, else α -> 1- yy
the response vector
- Definition Classes
- ExpSmoothing → Predictor
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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