object ForecasterVec
The ForecasterVec companion object provides functions useful for forecasting
models. It also contains a sample dataset.
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- val MAX_LAGS: Int
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def
differ(u: VectoD, v: VectoD, scale: Double = 1E-9, allow: Boolean = true): Int
Point out the differences between two vectors/time-series.
Point out the differences between two vectors/time-series.
- u
the first vector/time-series
- v
the second vector/time-series
- scale
the scale factor to set the tolerance 'tol'
- allow
flag indicating whether allow (via assert) any differences
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def
durbinLevinson(g: VectoD, ml: Int): MatriD
Apply the Durbin-Levinson Algorithm to iteratively compute the 'psi' matrix.
Apply the Durbin-Levinson Algorithm to iteratively compute the 'psi' matrix. The last row of the matrix gives 'AR' coefficients.
- g
the auto-covariance vector (gamma)
- ml
the maximum number of lags
- See also
www.stat.tamu.edu/~suhasini/teaching673/time_series.pdf, p. 247
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def
test(modName: String, p: Int, mod: ForecasterVec, kt: Int = 5, h: Int = 1): Unit
Test forecasting model that extends
ForecasterVec.Test forecasting model that extends
ForecasterVec.- modName
the name of the model to test
- p
the order of the model (number of response values to use in forecasts)
- mod
the model to test
- kt
the retraining frequency
- h
the forecasting horizon
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def
testInSamp(y: VectoD, pq: Int, mod: ForecasterVec, h: Int = 1): MatriD
Test (in sample) forecasting model that extends
ForecasterVec.Test (in sample) forecasting model that extends
ForecasterVec. returning the matrix of all forecasts over horizon and time.- y
the time series data
- pq
the order of the model (number of response values to use in forecasts)
- mod
the model to test
- h
the forecasting horizon
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