The ForecasterX trait provides a common framework for several forecasting models that use 1 ENDOGENOUS variable y and 0 or more EXOGENOUS variables xj. It provides methods for multi-horizon (1 to h) forecasting using the RECURSIVE technique. Forecasted values are produced only for the endogenous variable y. Lower case indicates actual values, while upper case is for forecasted values.
Y_t+1 = f(y_t, y_t-1, ... y_t-p+1, x0_t, x0_t-1, ... x0_t-p+1, x1_t, ...) Y_t+2 = f(Y_t+1, y_t, ... y_t-p+2, x0_t, x0_t-1, ... x0_t-p+1, x1_t, ...) ... Y_t+h = f(Y_t+1, y_t, ... y_t-p+2, x0_t, x0_t-1, ... x0_t-p+1, x1_t, ...)
Value parameters
- lags
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the lags (p) used for endogenous variable (e.g., 10 => use lags 1 to 10)
Attributes
- See also
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Forecaster - when there are no exogenous variables
- Companion
- object
- Graph
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- Supertypes
- Known subtypes
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class ARXclass ARX_Quadclass RegressionTreeGB4TSclass RegressionTreeMT4TSclass RegressionTreeRF4TSclass RegressionTreeRF_MT4TSShow 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+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+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
- yf
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the forecast matrix for the endogenous variable y (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
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the forecasting horizon, number of steps ahead to produce forecasts
- yf
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the forecast matrix for the endogenous variable y (time x horizons)
- yx
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the matrix of endogenous y and exogenous x values
Attributes
Get the internally row trimed and column expanded input matrix and response vector.
Get the internally row trimed and column expanded input matrix and response vector.
Attributes
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
- yx
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the matrix of endogenous y and exogenous x values
Attributes
Test FORECASTS at horizon h of a forecasting model y_ = f(lags (y_), x) + e and RETURN (1) aligned actual values, (2) the 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 at horizon h of a forecasting model y_ = f(lags (y_), x) + e and RETURN (1) aligned actual values, (2) the 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
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the forecasting horizon, number of steps ahead to produce forecasts
- y_
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the testing/full response/output vector
- yx
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the matrix of endogenous y and exogenous x values
Attributes
Concrete methods
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.
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.
Value parameters
- h
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the maximum forecasting horizon, number of steps ahead to produce forecasts
- y_
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the actual values to use in making forecasts
- yx
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the matrix of endogenous y and exogenous x values
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
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 and Abstract methods
Attributes
- Inherited from:
- Model
Return the feature/variable names.
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
- Inherited from:
- Model
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
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
Predict the value of y = f(z) by evaluating the model equation. Single output models return Double, while multi-output models return VectorD.
Predict the value of y = f(z) by evaluating the model equation. Single output models return Double, while multi-output models return VectorD.
Value parameters
- z
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the new vector to predict
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 classess 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 classess should implement various diagnostics for the test and full (training + test) datasets.
Value parameters
- x_
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the testiing/full data/input matrix (impl. classes may default to x)
- y_
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the testiing/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- Model
Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.
Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.
Value parameters
- x_
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the training/full data/input matrix (impl. classes may default to x)
- y_
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the training/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- Model
Inherited fields
The optional reference to an ontological concept