The VAR class provides multi-variate time series analysis capabilities for VAR models. VAR models are similar to ARX models, except that the exogenous variables are treated as endogenous variables and are themselves forecasted. Potentially having more up-to-date forecasted values feeding into multi-horizon forecasting can improve accuracy, but may also lead to compounding of forecast errors. Given multi-variate time series data where matrix x holds the input and matrix y holds the output, the next vector value y_t = combination of last p vector values in x.
y_t = bb dot x_t + e_t
where y_t is the value of y at time t, bb is the parameter matrix and e_t is the residual/error term.
Value parameters
- bakcast
-
whether a backcasted value is prepended to the time series (defaults to false)
- fname
-
the feature/variable names
- hh
-
the maximum forecasting horizon (h = 1 to hh)
- hparam
-
the hyper-parameters (defaults to
MakeMatrix4TS.hp) - tRng
-
the time range, if relevant (time index may suffice)
- x
-
the input lagged time series data
- y
-
the response/output matrix (multi-variate time series data)
Attributes
- Companion
- object
- Graph
-
- Supertypes
-
class Forecaster_RegVtrait Modeltrait FeatureSelectiontrait ForecastTensorclass Diagnosertrait Fittrait FitMclass Objecttrait Matchableclass AnyShow all
Members list
Value members
Concrete methods
Forge a new vector from the first spec values of x, the last p-h+1 values of x (past values), values 1 to h-1 from the forecasts, and available values from exogenous variables.
Forge a new vector from the first spec values of x, the last p-h+1 values of x (past values), values 1 to h-1 from the forecasts, and available values from exogenous variables.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- xx
-
the t-th row of the input matrix (lagged actual values)
- yy
-
the t-th row of the forecast tensor (forecasted future values)
Attributes
Inherited methods
Make the PREDICTION INTERVAL (PI) lower and upper bound vectors from the point predictions and the interval half widths.
Make the PREDICTION INTERVAL (PI) lower and upper bound vectors from the point predictions and the interval half widths.
Value parameters
- ihw
-
the vector of interval half widths (one for each prediction)
- yp
-
the vector of point predictions (y-hat)
Attributes
- Inherited from:
- Fit
Align the actual response matrix for comparison with the predicted/forecasted response matrix, returning a time vector and sliced response matrix.
Align the actual response matrix for comparison with the predicted/forecasted response matrix, returning a time vector and sliced response matrix.
Value parameters
- tr_size
-
the size of the intial training set
- y
-
the actual response for the full dataset (to be sliced)
Attributes
- Inherited from:
- Forecaster_RegV
Perform BACKWARD ELIMINATION to find the LEAST predictive features/variables to REMOVE from the full model, returning the features/variables left and the new Quality of Fit (QoF) measures/metrics for all steps.
Perform BACKWARD ELIMINATION to find the LEAST predictive features/variables to REMOVE from the full model, returning the features/variables left and the new Quality of Fit (QoF) measures/metrics for all steps.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- first
-
first variable to consider for elimination
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- Forecaster_RegV
Perform BEAM SEARCH SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the top k sets of features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, iterate over the models in the beam (top k) and create candidates by adding features (phase 1) and then removing (phase 2). From all the candidates, keep the best k and start a new iteration. Stops when there is no improvement in any of top k (or the maximum number of features is reached.
Perform BEAM SEARCH SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the top k sets of features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, iterate over the models in the beam (top k) and create candidates by adding features (phase 1) and then removing (phase 2). From all the candidates, keep the best k and start a new iteration. Stops when there is no improvement in any of top k (or the maximum number of features is reached.
Value parameters
- bk
-
the beam width holding the top k models (defaults to 3)
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- Forecaster_RegV
Build a sub-model that is restricted to the given columns of the data matrix. Must be implemented for models that support feature selection. Otherwise, use @see `NoBuildModel
Build a sub-model that is restricted to the given columns of the data matrix. Must be implemented for models that support feature selection. Otherwise, use @see `NoBuildModel
Value parameters
- fname2
-
the variable/feature names for the new model (defaults to null)
- x_cols
-
the columns that the new model is restricted to
Attributes
- Inherited from:
- Forecaster_RegV
Convert the underlying Regression Model to a subtype of Forecaster_Reg Forecasting Model.
Convert the underlying Regression Model to a subtype of Forecaster_Reg Forecasting Model.
Value parameters
- mod
-
the model to convert, e.g., the best model after feature selection
Attributes
- Inherited from:
- Forecaster_RegV
Attributes
- Inherited from:
- Forecaster_RegV
Diagnose the quality of the model for each variable.
Diagnose the quality of the model for each variable.
Value parameters
- yp
-
the matrix of predicted values
- yy
-
the matrix of actual values
Attributes
- Inherited from:
- Forecaster_RegV
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. For time series, the first few predictions use only part of the model, so may be skipped.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. For time series, the first few predictions use only part of the model, so may be skipped.
Value parameters
- w
-
the weights on the instances (defaults to null)
- y
-
the actual response/output vector to use (test/full)
- yp
-
the predicted response/output vector (test/full)
Attributes
- Definition Classes
- Inherited from:
- Diagnoser
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, for all horizons and print the results in a table. For time series, the first few predictions use only part of the model, so may be skipped. The version is for models that perform DIRECT multi-horizon forecasting.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, for all horizons and print the results in a table. For time series, the first few predictions use only part of the model, so may be skipped. The version is for models that perform DIRECT multi-horizon forecasting.
Value parameters
- yf
-
the entire FORECAST TENSOR
- yy
-
the actual response/output matrix over all horizons
Attributes
- Inherited from:
- ForecastTensor
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, for all horizons and print the results in a table. For time series, the first few predictions use only part of the model, so may be skipped. The version is for models that perform RECURSIVE multi-horizon forecasting.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, for all horizons and print the results in a table. For time series, the first few predictions use only part of the model, so may be skipped. The version is for models that perform RECURSIVE multi-horizon forecasting.
Value parameters
- rRng
-
the time range, defaults to null (=> full time range)
- sft
-
the amount of shift for yfh (FIX - ideally unify the code and remove sft)
- y_
-
the actual multi-variate time series matrix to use in making forecasts
- yf
-
the entire FORECAST TENSOR
Attributes
- Inherited from:
- ForecastTensor
Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. This method also includes PREDICTION INTERVAL (PI) metrics/measures.
Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. This method also includes PREDICTION INTERVAL (PI) metrics/measures.
Value parameters
- low_up
-
the predicted (lower, upper) bounds vectors
- w
-
the weights on the instances (defaults to null)
- y
-
the actual response/output vector to use (test/full)
- yp
-
the point prediction mean/median
- α
-
the significance/nominal level of uncertainty (α) (defaults to 0.1, 10%)
Attributes
- See also
-
otexts.com/fpp2/prediction-intervals.html Note:
wisshould be computed separately as the bounds are matrices. - Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the predicted & actual matrix responses (output variable per column). For some models the instances may be weighted.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the predicted & actual matrix responses (output variable per column). For some models the instances may be weighted.
Value parameters
- w
-
the weights on the instances (defaults to null)
- yy
-
the actual response/output matrix to use (test/full)
- yyp
-
the predicted response/output matrix (test/full)
Attributes
- See also
-
Regression_WLS - Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures for both POINT PREDICTIONS and PREDICTION INTERVALS.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures for both POINT PREDICTIONS and PREDICTION INTERVALS.
Value parameters
- iα
-
the index for the main significance level out of the vector α
- low_up
-
the predicted (lower, upper) bounds matrices for various α levels (column for each α level)
- y
-
the given time-series (must be aligned with the interval forecast)
- yp
-
the point prediction mean/median
- α
-
the vector of significance levels (defaults to the K = 11 prediction intervals used by the CDC Forecast Hub)
Attributes
- Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, specifically for the weighted interval score that allows using custom α levels.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, specifically for the weighted interval score that allows using custom α levels.
Value parameters
- low_up
-
the predicted (lower, upper) bounds matrices for various α levels (column for each α level)
- y
-
the given time-series (must be aligned with the interval forecast)
- yp
-
the point prediction mean/median
- α
-
the vector of significance levels (defaults to the K = 11 prediction intervals used by the CDC Forecast Hub)
Attributes
- Inherited from:
- Fit
Return the diagonalized version of the forecst tensor, i.e., for each of the j-th forecast matrices each row is at a fixed time point and, for example, the random walk model simply pushes the values down diagonals. Note 'unshiftDiag' reverses the process.
Return the diagonalized version of the forecst tensor, i.e., for each of the j-th forecast matrices each row is at a fixed time point and, for example, the random walk model simply pushes the values down diagonals. Note 'unshiftDiag' reverses the process.
Value parameters
- yf
-
the current forecast tensor
Attributes
- Inherited from:
- ForecastTensor
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 Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.
Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.
Attributes
- Definition Classes
- Inherited from:
- Fit
Produce a vector of size hh, h = 1 to hh-steps ahead forecasts for the model, i.e., forecast the following time points: t+1, ..., t+h. Intended to work with rolling validation (analog of predict method).
Produce a vector of size hh, h = 1 to hh-steps ahead forecasts for the model, i.e., forecast the following time points: t+1, ..., t+h. Intended to work with rolling validation (analog of predict method).
Value parameters
- t
-
the time point from which to make forecasts
- y_
-
the actual values to use in making predictions
Attributes
- Inherited from:
- Forecaster_RegV
Forecast values for all y_.dim time points and all horizons (1 through hh-steps ahead). Record these in the FORECAST TENSOR yf, where
Forecast values for all y_.dim time points and all horizons (1 through hh-steps ahead). Record these in the FORECAST TENSOR yf, where
yf(t, h) = h-steps ahead forecast for y_t
Value parameters
- y_
-
the actual values to use in making forecasts
Attributes
- Inherited from:
- Forecaster_RegV
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign into FORECAST TENSOR and return the h-steps ahead forecast. Note, yf(t, h, j) if the forecast to time t, horizon h, variable j Note, predictAll provides predictions for h = 1.
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign into FORECAST TENSOR and return the h-steps ahead forecast. Note, yf(t, h, j) if the forecast to time t, horizon h, variable j 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
Attributes
- See also
-
forecastAllmethod inForecastertrait. - Inherited from:
- Forecaster_RegV
Perform FORWARD SELECTION to find the MOST predictive features/variables to ADD into the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.
Perform FORWARD SELECTION to find the MOST predictive features/variables to ADD into the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- Forecaster_RegV
Return the best model found from feature selection.
Return the feature/variable names.
Get the data/input matrix built from lagged y vector (and optionally xe) values.
Get the data/input matrix built from lagged y vector (and optionally xe) values.
Attributes
- Inherited from:
- Forecaster_RegV
Return the data matrix x concatenated with response vector y.
Return the used response vector y (first colum in matrix).
Return the used response vector y (first colum in matrix).
Attributes
- Inherited from:
- Forecaster_RegV
Return the y-transformation.
Return the used response matrix y. Mainly for derived classes where y is transformed.
Return the used response matrix y. Mainly for derived classes where y is transformed.
Attributes
- Definition Classes
- Inherited from:
- Forecaster_RegV
Return the used FORECAST TENSOR yf.
Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit trait. Override to correspond to fitLabel.
Return the hyper-parameters.
Perform In-Sample Testing, i.e., train and test on the full data set. Return the prediction and the Quality of Fit.
Perform In-Sample Testing, i.e., train and test on the full data set. Return the prediction and the Quality of Fit.
Value parameters
- showYf
-
whether to show the forecast matrix FIX - copied from
Forecasterchange it to work for VAR, VARX - skip
-
the number of initial time points to skip (due to insufficient past)
Attributes
- Definition Classes
- Inherited from:
- Forecaster_RegV
The log-likelihood function times -2. Override as needed.
The log-likelihood function times -2. Override as needed.
Value parameters
- ms
-
raw Mean Squared Error
- s2
-
MLE estimate of the population variance of the residuals
Attributes
- See also
- Inherited from:
- Fit
Make the full FORECAST TENSOR where (for each sheet j) the zeroth column holds the actual time series and the last column is its time/time index. Columns 1, 2, ... hh are for h steps ahead forecasts.
Make the full FORECAST TENSOR where (for each sheet j) the zeroth column holds the actual time series and the last column is its time/time index. Columns 1, 2, ... hh are for h steps ahead forecasts.
Value parameters
- hh
-
the maximum forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual multi-variate times values to use in making forecasts
Attributes
- Inherited from:
- ForecastTensor
Models need to provide a means for updating the Degrees of Freedom (DF). Note: Degrees of Freedom are mainly relevant for full and train, not test.
Models need to provide a means for updating the Degrees of Freedom (DF). Note: Degrees of Freedom are mainly relevant for full and train, not test.
Value parameters
- size
-
the size of dataset (full, train, or test sets)
Attributes
- Inherited from:
- Diagnoser
Get the model name.
Return the mean of the squares for error (sse / df). Must call diagnose first.
Return the mean of the squares for error (sse / df). Must call diagnose first.
Attributes
- Inherited from:
- Fit
Return the parameters.
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
-
the new vector to predict
Attributes
- Inherited from:
- Forecaster_RegV
Predict a value for y_t using the 1-step ahead forecast.
Predict a value for y_t using the 1-step ahead forecast.
Value parameters
- t
-
the time point being predicted
- y_
-
the actual values to use in making predictions FIX --
Forecaster_Reguses x(t) while x(t-1) is used here
Attributes
- See also
-
modeling.rectifydefine inPredictor.scala - Inherited from:
- Forecaster_RegV
Predict all values corresponding to the given time series vector y_. Update FORECAST TENSOR yf and return PREDICTION MATRIX yp as second (1) column of yf with last value removed. Note, yf(t, h, j) if the forecast to time t, horizon h, variable j
Predict all values corresponding to the given time series vector y_. Update FORECAST TENSOR yf and return PREDICTION MATRIX yp as second (1) column of yf with last value removed. Note, yf(t, h, j) if the forecast to time t, horizon h, variable j
Value parameters
- y_
-
the actual time series values to use in making predictions
Attributes
- See also
-
forecastAllto forecast beyond horizon h = 1.Forecaster.predictAllfor template implementation for vectors - Inherited from:
- Forecaster_RegV
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat).
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat).
Value parameters
- df_
-
the error/residual degrees of freedom
- x_
-
the testing/full data/input matrix
- α
-
the significance level α = .1 for TWO TAILS: left tail .05 | 1 - α = .90 | .05 right tail e.g., for AutoMPG, t_crit (385, 0.90) = 1.6488210657096942 t_crit (385, 0.95) = 1.966
Attributes
- See also
-
predictCIntinPredictorstats.stackexchange.com/questions/585660/what-is-the-formula-for-prediction-interval-in-multivariate-case
- Inherited from:
- Fit
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat) and each significance level.
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat) and each significance level.
Value parameters
- df_
-
the error/residual degrees of freedom
- x_
-
the testing/full data/input matrix
- α
-
the significance levels to be used (defaults to
Fit.α_)
Attributes
- Inherited from:
- Fit
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
-
the Quality of Fit (QoF) results
Attributes
- Inherited from:
- Model
Return the coefficient of determination (R^2). Must call diagnose first.
Return the coefficient of determination (R^2). Must call diagnose first.
Attributes
- Inherited from:
- FitM
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
-
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
-
the vector of qof values produced by the
Fittrait
Attributes
- Inherited from:
- Model
Reset the best-step to default
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
Value parameters
- df_update
-
the updated degrees of freedom (regression/model, error)
Attributes
- Inherited from:
- Fit
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TRAINING SET (tr) and a TESTING SET (te). as follows: [ <-- tr_size --> | <-- te_size --> ] Calls forecast for h-steps ahead out-of-sample forecasts. Return the FORECAST TENSOR.
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TRAINING SET (tr) and a TESTING SET (te). as follows: [ <-- tr_size --> | <-- te_size --> ] Calls forecast for h-steps ahead out-of-sample forecasts. Return the FORECAST TENSOR.
Value parameters
- growing
-
whether the training grows as it roll or kepps a fixed size FIX - copied from
Forecasterchange it to work for VAR, VARX - rc
-
the retraining cycle (number of forecasts until retraining occurs)
Attributes
- Inherited from:
- Forecaster_RegV
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
-
the variable/column dependency measure (defaults to correlation)
- thr1
-
the threshold used to compare the predictor x-columns to the y-column only want variables above some minimal dependency level
- thr2
-
the threshold used to compare the predictor x-columns with each other only want variables below some cut-off dependency/collinearity level
- xy
-
the [ x, y ] combined data-response matrix
Attributes
- Inherited from:
- Model
Perform feature selection to find the most predictive features/variables to have in the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.
Perform feature selection to find the most predictive features/variables to have in the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- first
-
first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
- swap
-
whether to allow a swap step (swap out a feature for a new feature in one step)
- tech
-
the feature selection technique to apply
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- FeatureSelection
Set the number of data points/elements to skip at the beginning of a time series for purposes of diagnosis or computing a loss function. For In-Sample, the first value (time t = 0) is not forecastable without backcasting.
Set the number of data points/elements to skip at the beginning of a time series for purposes of diagnosis or computing a loss function. For In-Sample, the first value (time t = 0) is not forecastable without backcasting.
Value parameters
- skip
-
skip this many elements at the beginning of the time series
Attributes
- Inherited from:
- Diagnoser
Show the QoF metrics/measures in vector qof.
Show the QoF metrics/measures in vector qof.
Value parameters
- qof
-
the QoF metrics (e.g., for point and interval predictions/forecasts)
Attributes
- Inherited from:
- Fit
Return the sum of the squares for error (sse). Must call diagnose first.
Return the sum of the squares for error (sse). Must call diagnose first.
Attributes
- Inherited from:
- FitM
Compute the sum of squares errors (loss function), assuming the first 'skip' errors are zero.
Compute the sum of squares errors (loss function), assuming the first 'skip' errors are zero.
Value parameters
- y
-
the actual response vector
- yp
-
the predicted response vector (one-step ahead)
Attributes
- Inherited from:
- Diagnoser
Perform STEPWISE SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, it calls forward and backward and takes the best of the two actions. Stops when neither action yields improvement.
Perform STEPWISE SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, it calls forward and backward and takes the best of the two actions. Stops when neither action yields improvement.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
- swap
-
whether to allow a swap step (swap out a feature for a new feature in one step)
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- Forecaster_RegV
Produce a QoF SUMMARY for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.
Produce a QoF SUMMARY for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.
Value parameters
- b
-
the parameters/coefficients for the model
- fname
-
the array of feature/variable names
- vifs
-
the Variance Inflation Factors (VIFs)
- x_
-
the testing/full data/input matrix
Attributes
- Definition Classes
- Inherited from:
- Fit
Get the type of the task performed by 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_
-
the testing/full data/input matrix (impl. classes may default to x)
- y_
-
the testing/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- Forecaster_RegV
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_
-
the data/input matrix (ignored, pass null)
- y_
-
the actual testing/full response/output matrix
Attributes
- Inherited from:
- Forecaster_RegV
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_
-
the training/full data/input matrix (impl. classes may default to x)
- y_
-
the training/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- Forecaster_RegV
Train/fit an Forecaster_RegV model to the times-series data y_ = f(x). Estimate the coefficient matrix bb for a Forecaster_RegV model. Uses OLS Matrix Factorization to determine the coefficients, i.e., the bb matrix.
Train/fit an Forecaster_RegV model to the times-series data y_ = f(x). Estimate the coefficient matrix bb for a Forecaster_RegV model. Uses OLS Matrix Factorization to determine the coefficients, i.e., the bb matrix.
Value parameters
- x_
-
the data/input matrix (e.g., full x)
- y_
-
the training/full response matrix (e.g., full y)
Attributes
- Inherited from:
- Forecaster_RegV
Train and test the forecasting model y_ = f(x_) + e and report its QoF and plot its predictions. Return the predictions and QoF. NOTE: must use trainNtest_x when an x matrix is used, such as in VAR.
Train and test the forecasting model y_ = f(x_) + e and report its QoF and plot its predictions. Return the predictions and QoF. NOTE: must use trainNtest_x when an x matrix is used, such as in VAR.
Value parameters
- x_
-
the training/full data/input matrix (defaults to full x)
- xx
-
the testing/full data/input matrix (defaults to full x)
- y_
-
the training/full response/output vector (defaults to full y)
- yy
-
the testing/full response/output vector (defaults to full y)
Attributes
- Inherited from:
- Forecaster_RegV
Attributes
- Inherited from:
- Forecaster_RegV
Inherited fields
The optional reference to an ontological concept