ForecastTensor
The ForecastTensor trait provides a common framework for holding forecasts over time and multiple horizons.
Value parameters
- hh
-
the maximum forecasting horizon (h = 1 to hh)
- tRng
-
the time vector, if relevant (index as time may suffice)
- y
-
the multi-variate time series data matrix [y_tj]
Attributes
- Graph
-
- Supertypes
-
class Objecttrait Matchableclass Any
- Known subtypes
Members list
Value members
Abstract methods
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)
- yfh
-
the predicted response/output vector (test/full)
Attributes
Models need to provide a means for updating the Degrees of Freedom (DF).
Models need to provide a means for updating the Degrees of Freedom (DF).
Value parameters
- size
-
the size of dataset (full, train, or test)
Attributes
Concrete methods
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
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
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
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