The Forecaster trait provides a common framework for several forecasters. Note, the train method must be called first followed by test.
Value parameters
- hparam
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the hyper-parameters for models extending this trait
- tt
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the time vector, if relevant (index as time may suffice)
- y
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the response vector (time series data)
Attributes
- Companion
- object
- Graph
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- Supertypes
- Known subtypes
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class ARclass AR1MAclass ARIMAclass ARMAclass NullModelclass QuadSplineclass RandomWalkclass SimpleExpSmoothingclass SimpleMovingAverageclass TrendModelclass WeightedMovingAverageShow all
Members list
Value members
Abstract methods
Produce a vector of size h, of 1 through h-steps ahead forecasts for the model. forecast the following time points: t+1, ..., t-1+h. Note, must create the yf matrix before calling the forecast method. Intended to work with rolling validation (analog of predict method)
Produce a vector of size h, of 1 through h-steps ahead forecasts for the model. forecast the following time points: t+1, ..., t-1+h. Note, must create the yf matrix before calling the forecast method. Intended to work with rolling validation (analog of predict method)
Value parameters
- h
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the forecasting horizon, number of steps ahead to produce forecasts
- t
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the time point from which to make forecasts
- y_
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the actual values to use in making predictions
- yf
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the forecast matrix (time x horizons)
Attributes
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign into FORECAST MATRIX and return the h-steps ahead forecast. Note, predictAll provides predictions for h = 1.
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign into FORECAST MATRIX and return the h-steps ahead forecast. Note, predictAll provides predictions for h = 1.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual values to use in making forecasts
- yf
-
the forecast matrix (time x horizons)
Attributes
- See also
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forecastAllmethod inForecastertrait.
Predict a value for y_t+1 using the 1-step ahead forecast. y_t+1 = f (y_t, ...) + e_t+1
Predict a value for y_t+1 using the 1-step ahead forecast. y_t+1 = f (y_t, ...) + e_t+1
Value parameters
- t
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the time point from which to make prediction
- y_
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the actual values to use in making predictions
Attributes
Test FORECASTS of a forecasting model y_ = f(lags (y_)) + e and RETURN (1) aligned actual values, (2) its forecasts and (3) QoF vector. Testing may be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train and forecastAll before testF.
Test FORECASTS of a forecasting model y_ = f(lags (y_)) + e and RETURN (1) aligned actual values, (2) its forecasts and (3) QoF vector. Testing may be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train and forecastAll before testF.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the testing/full response/output vector
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
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
As seen from class WeightedMovingAverage, the missing signatures are as follows. For convenience, these are usable as stub implementations.
As seen from class WeightedMovingAverage, the missing signatures are as follows. For convenience, these are usable as stub implementations.
Attributes
Forecast values for all y_.dim time points and all horizons (1 through h-steps ahead). Record these in the FORECAST MATRIX yf, where yf(t, k) = k-steps ahead forecast for y_t Note, column 0, yf(?, 0), is set to y (the actual time series values). last column, yf(?, h+1), is set to t (the time values, for reference). Forecast recursively down diagonals in the yf forecast matrix. The top right and bottom left triangles in yf matrix are not forecastable. FIX - merge the forecast matrices used by predictAll and forecastAll.
Forecast values for all y_.dim time points and all horizons (1 through h-steps ahead). Record these in the FORECAST MATRIX yf, where yf(t, k) = k-steps ahead forecast for y_t Note, column 0, yf(?, 0), is set to y (the actual time series values). last column, yf(?, h+1), is set to t (the time values, for reference). Forecast recursively down diagonals in the yf forecast matrix. The top right and bottom left triangles in yf matrix are not forecastable. FIX - merge the forecast matrices used by predictAll and forecastAll.
Value parameters
- h
-
the maximum forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual values to use in making forecasts
Attributes
Forecast intervals for all y_.dim time points at horizon h (h-steps ahead). Create prediction intervals (two vectors) for the given time points at level p. Caveat: assumes errors follow a Normal distribution. Override this method to handle other cases.
Forecast intervals for all y_.dim time points at horizon h (h-steps ahead). Create prediction intervals (two vectors) for the given time points at level p. Caveat: assumes errors follow a Normal distribution. Override this method to handle other cases.
Value parameters
- h
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the forecasting horizon, number of steps ahead to produce forecasts
- p
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the level (1 - alpha) for the prediction interval
- y_
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the aligned actual values to use in making forecasts
- yfh
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the forecast vector at horizon h
Attributes
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly. Note, all lags up and including 'p|q' define the model.
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly. Note, all lags up and including 'p|q' define the model.
Value parameters
- cols
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the lags/columns currently included in the existing model (currently ignored)
- idx_q
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index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
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Fitfor index of QoF measures.
Perform forward selection to find the most predictive lags/variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Perform forward selection to find the most predictive lags/variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Value parameters
- cross
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whether to include the cross-validation QoF measure (currently ignored)
- idx_q
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index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
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Fitfor index of QoF measures.
Return the feature/variable names. Override for models like SARIMAX.
Return the feature/variable names. Override for models like SARIMAX.
Attributes
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.
Attributes
Return the used response vector y. Used by derived classes where y may be transformed, e.g., ARX.
Return the used response vector y. Used by derived classes where y may be transformed, e.g., ARX.
Attributes
Return the FORECAST MATRIX yf (initially allocated in predictAll method).
Return the FORECAST MATRIX yf (initially allocated in predictAll method).
Attributes
Return the hyper-parameters.
Return the hyper-parameters.
Attributes
Make the full FORECAST MATRIX from the prediction forecast matrix (built by prodictAll). Has has more columns and a few more rows and copies all contents from the prediction forecast matrix.
Make the full FORECAST MATRIX from the prediction forecast matrix (built by prodictAll). Has has more columns and a few more rows and copies all contents from the prediction forecast matrix.
Value parameters
- h
-
the maximum forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual values to use in making forecasts
- yp_
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the predicted values (h=1) to use in making forecasts
Attributes
Return the vector of parameter/coefficient values (they are model specific). Override for models with parameters.
Return the vector of parameter/coefficient values (they are model specific). Override for models with parameters.
Attributes
The standard signature for prediction does not apply to time series.
The standard signature for prediction does not apply to time series.
Attributes
Predict all values corresponding to the given time series vector y_. Create FORECAST MATRIX yf and return PREDICTION VECTOR yp as second (1) column of yf with last value removed.
Predict all values corresponding to the given time series vector y_. Create FORECAST MATRIX yf and return PREDICTION VECTOR yp as second (1) column of yf with last value removed.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual time series values to use in making predictions
Attributes
- See also
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forecastAllto forecast beyond horizon h = 1.
Return the vector of residuals/errors.
Return the vector of residuals/errors.
Attributes
Test PREDICTIONS of a forecasting model y_ = f(lags (y_)) + e and return its predictions and QoF vector. Testing may be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test. Must override to get Quality of Fit (QoF).
Test PREDICTIONS of a forecasting model y_ = f(lags (y_)) + e and return its predictions and QoF vector. Testing may be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test. Must override to get Quality of Fit (QoF).
Value parameters
- x_null
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the data/input matrix (ignored, pass null)
- y_
-
the actual testing/full response/output vector
Attributes
Train and test the forecasting model y_ = f(y-past) + e and report its QoF and plot its predictions. Return the predictions and QoF.
Train and test the forecasting model y_ = f(y-past) + e and report its QoF and plot its predictions. Return the predictions and QoF.
Value parameters
- y_
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the training/full response/output vector (defaults to full y)
- yy
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the testing/full response/output vector (defaults to full y)
Attributes
Inherited methods
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
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
Inherited fields
The optional reference to an ontological concept