The Forecaster companion object provides methods useful for classes extending the Forecaster abstract class, i.e., forecasting models with a single input variable.
Attributes
- Companion
- class
- Graph
-
- Supertypes
-
class Objecttrait Matchableclass Any
- Self type
-
Forecaster.type
Members list
Value members
Concrete methods
Point out the differences between two vectors/time series.
Point out the differences between two vectors/time series.
Value parameters
- allow
-
flag indicating whether allow (via assert) any differences
- scale
-
the scale factor to set the tolerance 'tol'
- u
-
the first vector/time series
- v
-
the second vector/time series
Attributes
Compute a reverse dot product of the parameter vector b and the most recent actual values in the time series y_, going backwards from y_t. Use max (0, ..) to avoid using negative indices into the y_ vector.
Compute a reverse dot product of the parameter vector b and the most recent actual values in the time series y_, going backwards from y_t. Use max (0, ..) to avoid using negative indices into the y_ vector.
Value parameters
- b
-
the parameter/coefficient vector (e.g., φ for AR)
- t
-
the time point FROM WHICH to make prediction
- y_
-
the actual time series values to use in making predictions
Attributes
Compute a reverse dot product of the parameter vector b and a row of the yf matrix starting at element (r, c) and moving back and then moving up. Use max (0, ..) to avoid using negative indices into the yf matrix.
Compute a reverse dot product of the parameter vector b and a row of the yf matrix starting at element (r, c) and moving back and then moving up. Use max (0, ..) to avoid using negative indices into the yf matrix.
Value parameters
- b
-
the parameter/coefficient vector (e.g., φ for AR)
- c
-
the starting column in the forecast matrix (horizon)
- r
-
the starting row in the forecast matrix (time)
- yf
-
the forecast matrix (time x horizons)
Attributes
Set the training ratio = ratio of training set to full dataset.
Set the training ratio = ratio of training set to full dataset.
Value parameters
- m
-
the size of the full dataset
Attributes
Return the index range for the testing set.
Return the index range for the testing set.
Value parameters
- m
-
the size of the full dataset
Attributes
Calculate the size (number of instances) for a testing set (round up).
Calculate the size (number of instances) for a testing set (round up).
Value parameters
- m
-
the size of the full dataset
Attributes
Calculate the size (number of instances) for a training set.
Calculate the size (number of instances) for a training set.
Value parameters
- m
-
the size of the full dataset