VAR
The VAR object supports regression for Multivariate Time Series data. Given a response matrix y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training. y_t = b dot x where x = [y_{t-1}, y_{t-2}, ... y_{t-lag}].
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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VAR.type
Members list
Value members
Concrete methods
Create a RegressionMV object from a response matrix. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Create a RegressionMV object from a response matrix. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Value parameters
- h
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the forecasting horizon (1, 2, ... h)
- hparam
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the hyper-parameters (use Regression.hp for default)
- intercept
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whether to add a column of all ones to the matrix (intercept)
- lags
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the maximum lag included (inclusive)
- y
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the original un-expanded output/response matrix
Attributes
Plot actual vs. predicted values for all variables (columns of the matrices).
Plot actual vs. predicted values for all variables (columns of the matrices).
Value parameters
- name
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the name of the model run to produce yp
- y
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the original un-expanded output/response matrix
- yp
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the predicted values (one-step ahead forecasts) matrix
Attributes
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TESTING SET (tr) and a TRAINING SET (te) as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls predict for one-step ahead out-of-sample forecasts.
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TESTING SET (tr) and a TRAINING SET (te) as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls predict for one-step ahead out-of-sample forecasts.
Value parameters
- mod
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the forecasting model being used (e.g.,
VAR) - rc
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the retraining cycle (number of forecasts until retraining occurs)
Attributes
- See also
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RollingValidation
Split the x matrix and y matrix into training and testing sets.
Split the x matrix and y matrix into training and testing sets.
Value parameters
- ratio
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the ratio of the TESTING set to the full dataset (most common 70-30, 80-20)
- x
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the x data/input matrix
- y
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the y response/output matrix