Forecast
The Forecast trait serves a minimal adpater of Model to the Forecast task. Most of implementation is in the Forecaster abstract class below.
Attributes
- Graph
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- Supertypes
- Known subtypes
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class AR_Starclass RandomWalk_Starclass Forecasterclass ARclass ARMAclass Forecaster_Dclass NeuralNet_3L4TSclass ARX_Dclass NARX_SR_Dclass ARX_Quad_Dclass ARX_SR_Dclass ARY_Dclass Forecaster_Regclass ARXclass ARX_Quadclass ARX_SRclass ARYclass ARY_Quadclass SARYclass NullModelclass RandomWalkclass RandomWalkSclass SimpleExpSmoothingclass SimpleMovingAverageclass WeightedMovingAverageclass SimpleMovingAverage2class TrendModelShow all
Members list
Value members
Concrete methods
Return the maximum lag used by the model (its capacity to look into the past). Models that use more than one past value to make predictions/forecasts must override this method, e.g., ARMA (2, 3) should set the cap to max(p, q) = 3.
Return the maximum lag used by the model (its capacity to look into the past). Models that use more than one past value to make predictions/forecasts must override this method, e.g., ARMA (2, 3) should set the cap to max(p, q) = 3.
Attributes
The standard signature for prediction does not apply to time series.
The standard signature for prediction does not apply to time series.
Attributes
Return the feature/variable names. Model that use x should override.
Return the feature/variable names. Model that use x should override.
Attributes
Return the used data/input matrix. Model that use x should override.
Return the used data/input matrix. Model that use x should override.
Attributes
The standard signature for prediction does not apply to time series.
The standard signature for prediction does not apply to time series.
Attributes
Given a time series y_, train the forecasting function y_ = f(lags (y_)) + e, where f(lags (y_)) is a function of the lagged values of y_, by fitting its parameters.
Given a time series y_, train the forecasting function y_ = f(lags (y_)) + e, where f(lags (y_)) is a function of the lagged values of y_, by fitting its parameters.
Value parameters
- x_null
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the data/input matrix (ignored, pass null)
- y_
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the testing/full response/output vector (e.g., full y)
Attributes
Inherited methods
Print the model prediction equation in readable form. Override per model.
Print the model prediction equation in readable form. Override per model.
Attributes
- Inherited from:
- Model
Print the model prediction equation in LaTex form. Override per model.
Print the model prediction equation in LaTex form. Override per model.
Attributes
- Inherited from:
- Model
Return the data matrix x concatenated with response vector y.
Return the used response matrix y, if needed.
Return the used response matrix y, if needed.
Attributes
- See also
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neuralnet.PredictorMV - Inherited from:
- Model
Get the model name.
Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.
Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.
Value parameters
- qof
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the Quality of Fit (QoF) results
Attributes
- Inherited from:
- Model
Return a basic report on a trained and tested multi-variate model.
Return a basic report on a trained and tested multi-variate model.
Value parameters
- ftMat
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the matrix of qof values produced by the
Fittrait
Attributes
- Inherited from:
- Model
Return a basic report on a trained and tested model.
Return a basic report on a trained and tested model.
Value parameters
- ftVec
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the vector of qof values produced by the
Fittrait
Attributes
- Inherited from:
- Model
Screen the x-columns of matrix xy based on the two thresholds, returning the reduced matrix and the column indices/predictor variables selected.
Screen the x-columns of matrix xy based on the two thresholds, returning the reduced matrix and the column indices/predictor variables selected.
Value parameters
- dep
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the variable/column dependency measure (defaults to correlation)
- thr1
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the threshold used to compare the predictor x-columns to the y-column only want variables above some minimal dependency level
- thr2
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the threshold used to compare the predictor x-columns with each other only want variables below some cut-off dependency/collinearity level
- xy
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the [ x, y ] combined data-response matrix
Attributes
- Inherited from:
- Model
Inherited and Abstract methods
Return the best model found from feature selection.
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.
Attributes
- Inherited from:
- Model
Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.
Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.
Attributes
- Inherited from:
- Model
Perform In-Sample Testing, i.e., train and test on the same FULL data set. Good for initial testing and understanding variable relationships. Return the prediction and the Quality of Fit.
Perform In-Sample Testing, i.e., train and test on the same FULL data set. Good for initial testing and understanding variable relationships. Return the prediction and the Quality of Fit.
Value parameters
- showYp
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whether to show the prediction vector
- skip
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the number of initial data points to skip (due to insufficient information)
Attributes
- Note
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May lead to over-fitting for complex models.
- Inherited from:
- Model
Return the vector of model parameter/coefficient values. Single output models have VectorD parameters, while multi-output models have MatrixD.
Return the vector of model parameter/coefficient values. Single output models have VectorD parameters, while multi-output models have MatrixD.
Attributes
- Inherited from:
- Model
Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classes should implement various diagnostics for the test and full (training + test) datasets.
Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classes should implement various diagnostics for the test and full (training + test) datasets.
Value parameters
- x_
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the testing/full data/input matrix (impl. classes may default to x)
- y_
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the testing/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- Model
Inherited fields
The optional reference to an ontological concept