Value parameters
- dd
-
the order of seasonal differencing
- hparam
-
the hyper-parameters
- period
-
the seasonal period (at least 2)
- tt
-
the time vector, if relevant (time index may suffice)
- x
-
the exogenous time series data as an input matrix
- y
-
the original endogenous input vector (time series data)
Attributes
- Companion
- object
- Graph
-
- Supertypes
-
class SARIMAclass ARIMAclass ARMAtrait Correlogramtrait Forecastertrait Modelclass FitItrait Fittrait FitMclass Objecttrait Matchableclass AnyShow all
Members list
Value members
Concrete methods
Return the maximum lag used by this model (its capacity to look into the past).
Return the maximum lag used by this model (its capacity to look into the past).
Attributes
- Definition Classes
Produce h-steps-ahead forecast for SARIMAX models.
Produce h-steps-ahead forecast for SARIMAX models.
Value parameters
- h
-
the number of steps to forecast, must be at least one
- t
-
the time point from which to make forecasts (in the original scale)
Attributes
- See also
-
ams.sunysb.edu/~zhu/ams586/Forecasting.pdf
- Definition Classes
Return the parameter vector (concatenation of φ, θ, φφ and θθ).
Return the parameter vector (concatenation of φ, θ, φφ and θθ).
Attributes
- Definition Classes
Return the vector of predicted/fitted values on the training/full data. Based on zp calculated in the updateFittedValues method.
Return the vector of predicted/fitted values on the training/full data. Based on zp calculated in the updateFittedValues method.
Value parameters
- y_
-
the given time-series
Attributes
- Definition Classes
Set values for the models orders p, q, pp and qq.
Set values for the models orders p, q, pp and qq.
Value parameters
- pq
-
the vector of model orders
Attributes
- Definition Classes
Train/fit an SARIMAX model to the times-series data in vector y_. Must call setPQ first. Estimate the coefficient vectors doer a SARIMAX(p, d, q, P, D, Q)_s model. It uses BFGS, a Quasi-Newton optimizer, to minimize the negative log-likelihood.
Train/fit an SARIMAX model to the times-series data in vector y_. Must call setPQ first. Estimate the coefficient vectors doer a SARIMAX(p, d, q, P, D, Q)_s model. It uses BFGS, a Quasi-Newton optimizer, to minimize the negative log-likelihood.
Value parameters
- x_null
-
the data/input matrix (ignored, pass null)
- y_
-
the training/full response vector
Attributes
- Definition Classes
Inherited methods
Return the autocorrelation vector (ACF).
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.
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.
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
- See also
-
Regression_WLS - Definition Classes
- Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoFI) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. Note: wis should be computed separately.
Diagnose the health of the model by computing the Quality of Fit (QoFI) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. Note: wis should be computed separately.
Value parameters
- alpha
-
the nominal level of uncertainty (alpha) (defaults to 0.9, 90%)
- low
-
the predicted lower bound
- up
-
the predicted upper bound
- 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
Attributes
- See also
-
Regression_WLS - Inherited from:
- FitI
Diagnose the health of the model by computing the Quality of FitI (QoFI) measures,
Diagnose the health of the model by computing the Quality of FitI (QoFI) measures,
Value parameters
- alphas
-
the array of prediction levels
- low
-
the lower bounds for various alpha levels
- up
-
the upper bounds for various alpha levels
- y
-
the given time-series (must be aligned with the interval forecast)
- yp
-
the point prediction mean/median
Attributes
- Inherited from:
- FitI
Apply the Durbin-Levinson Algorithm to iteratively compute the psi matrix. The last/p-th row of the matrix gives AR coefficients. Note, also known as Levinson-Durbin.
Apply the Durbin-Levinson Algorithm to iteratively compute the psi matrix. The last/p-th row of the matrix gives AR coefficients. Note, also known as Levinson-Durbin.
Value parameters
- g
-
the auto-covariance vector (gamma)
- ml
-
the maximum number of lags
Attributes
- See also
- Inherited from:
- Correlogram
Return the Quality of FitI (QoFI) measures corresponding to the labels given. Override to add more quality of fit measures.
Produce h-steps-ahead forecast for ARIMA models.
Produce h-steps-ahead forecast for ARIMA models.
Value parameters
- h
-
the number of steps to forecast, must be at least one
- t
-
the time point from which to make forecasts (in the original scale)
Attributes
- See also
-
ams.sunysb.edu/~zhu/ams586/Forecasting.pdf
- Inherited from:
- ARIMA
Forecast values for all time points using 1 through h-steps ahead forecasts. The h-th row of matrix is the horizon h forecast (where h = 0 is actual data).
Forecast values for all time points using 1 through h-steps ahead forecasts. The h-th row of matrix is the horizon h forecast (where h = 0 is actual data).
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
Attributes
- Definition Classes
-
ARIMA -> Forecaster
- Inherited from:
- ARIMA
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign to forecasting matrix and return h-step ahead forecast.
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign to forecasting matrix and return h-step ahead forecast.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual values to use in making forecasts
- yf
-
the forecasting matrix (time x horizons)
Attributes
- See also
-
forecastAll method in
Forecastertrait - Definition Classes
-
ARMA -> Forecaster
- Inherited from:
- ARMA
Forecast intervals for all y_.dim time points at horizon h (h-steps ahead). Create prediction intervals (two vectors) for the given time points at level p. Caveat: assumes errors follow a Normal distribution. Override this method to handle other cases.
Forecast intervals for all y_.dim time points at horizon h (h-steps ahead). Create prediction intervals (two vectors) for the given time points at level p. Caveat: assumes errors follow a Normal distribution. Override this method to handle other cases.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- p
-
the level (1 - alpha) for the prediction interval
- y_
-
the aligned actual values to use in making forecasts
- yfh
-
the forecast vector at horizon h
Attributes
- Inherited from:
- Forecaster
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly. Note, all lags up and including 'p|q' define the model.
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly. Note, all lags up and including 'p|q' define the model.
Value parameters
- cols
-
the lags/columns currently included in the existing model (currently ignored)
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- Forecaster
Perform forward selection to find the most predictive lags/variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Perform forward selection to find the most predictive lags/variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Value parameters
- cross
-
whether to include the cross-validation QoF measure (currently ignored)
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- Forecaster
Return the feature/variable names. Override for models like SARIMAX.
Return the feature/variable names. Override for models like SARIMAX.
Attributes
- Inherited from:
- Forecaster
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., ARX.
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., ARX.
Attributes
- Inherited from:
- Forecaster
Return the help string that describes the Quality of FitI (QoFI) measures provided by the FitI class. Override to correspond to fitLabel.
Return the hyper-parameters.
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 a Correlogram, i.e., compute stats, psi and pacf.
Make a Correlogram, i.e., compute stats, psi and pacf.
Value parameters
- y_
-
the current (e.g., training) times-series to use (defaults to full y)
Attributes
- Inherited from:
- Correlogram
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
Attributes
- Inherited from:
- Forecaster
Return the partial autocorrelation vector (PACF).
Plot a function, e.g., Auto-Correlation Function (ACF), Partial Auto-Correlation Function (PACF) with confidence bound.
Plot a function, e.g., Auto-Correlation Function (ACF), Partial Auto-Correlation Function (PACF) with confidence bound.
Value parameters
- fVec
-
the vector given function values
- name
-
the name of the function
- show
-
whether to show the fVec values
Attributes
- Inherited from:
- Correlogram
Predict a value for y_t+1 using 1-step ahead forecasts (p' = p-1). y_t+1 = δ + φ_0 y_t + φ_1 y_t-1 + ... + φ_p' y_t-p' + θ_0 e_t + θ_1 e_t-1 + ... + θ_q' e_t-q' When k < 0 let y_k = y_0 (i.e., assume first value repeats back in time), but do not assume errors repeat.
Predict a value for y_t+1 using 1-step ahead forecasts (p' = p-1). y_t+1 = δ + φ_0 y_t + φ_1 y_t-1 + ... + φ_p' y_t-p' + θ_0 e_t + θ_1 e_t-1 + ... + θ_q' e_t-q' When k < 0 let y_k = y_0 (i.e., assume first value repeats back in time), but do not assume errors repeat.
Value parameters
- t
-
the time point/index to be predicted
- y_
-
the actual values to use in making predictions
Attributes
- See also
-
predictAll method in
Forecastertrait - Definition Classes
-
ARMA -> Forecaster
- Inherited from:
- ARMA
The standard signature for prediction does not apply to time-series.
The standard signature for prediction does not apply to time-series.
Attributes
- Inherited from:
- Forecaster
Return the psi matrix.
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 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 (model, error)
Attributes
- Inherited from:
- Fit
Return the vector of residuals/errors.
Obtain residuals/errors in the original scale.
Show estimates for parameters.
Show the prediction interval forecasts and relevant QoF metrics/measures.
Show the prediction interval forecasts and relevant QoF metrics/measures.
Value parameters
- h
-
the forecasting horizon
- low
-
the predicted lower bound
- qof_all
-
all the QoF metrics (for point and interval forecasts)
- up
-
the predicted upper bound
- yfh
-
the forecasts for horizon h
- yy
-
the aligned actual response/output vector to use (test/full)
Attributes
- Inherited from:
- FitI
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
Return basic statistics on time-series y or y_.
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
- Inherited from:
- Fit
Compute the error (difference between actual and predicted) and useful diagnostics for the dataset.
Compute the error (difference between actual and predicted) and useful diagnostics for the dataset.
Value parameters
- y
-
vector of observed values
- yp
-
vector of predicted values
Attributes
- Definition Classes
- Inherited from:
- ARIMA
Test FORECASTS of an ARMA forecasting model y_ = f(lags (y_)) + e and return its forecasts 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 and forecastAll before testF.
Test FORECASTS of an ARMA forecasting model y_ = f(lags (y_)) + e and return its forecasts 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 and forecastAll before testF.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the testing/full response/output vector
Attributes
- Inherited from:
- ARMA
Train and test the forecasting model y_ = f(y-past) + e and report its QoF and plot its predictions. Return the predictions and QoF.
Train and test the forecasting model y_ = f(y-past) + e and report its QoF and plot its predictions. Return the predictions and QoF.
Value parameters
- 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
Inherited fields
The optional reference to an ontological concept