GP-NARX models¶
Example¶
Class Arguments¶
The GPNARX() model class has the following arguments:
- data : requires a pd.DataFrame object or an np.array
- ar : the number of autoregressive lags
- kernel_type: the type of kernel; one of [‘SE’,’RQ’,’OU’,’ARD’]
- integ : (default : 0) order of integration (0 : no difference, 1 : first difference, ...)
- target : (default: None) specify the pandas column name or numpy index if the input is a matrix. If None, the first column will be chosen as the data.
Class Attributes¶
A GPNARX() object holds the following attributes:
Model Attributes:
- ar : the number of autoregressive lags
- integ : order of integration (0 : no difference, 1 : first difference, ...)
- param_no : number of model parameters
- kernel_type : the string name of the kernel
- kernel : the kernel object (and associated methods)
- norm_mean : the mean of the (differenced) time series
- norm_std : the standard deviation of the (differenced) time series
- index : the timescale of the time-series
- data : the dependent variable held as a np.array
- data_name : string variable containing name of the time series
- data_type : whether original datatype is numpy or pandas
Parameter Attributes:
The attribute param.desc is a dictionary holding information about individual parameters:
- name : name of the parameter
- index : index of the parameter (begins with 0)
- prior : the prior specification for the parameter
- q : the variational distribution approximation
Inference Attributes:
- params : holds any estimated parameters
- ses : holds any estimated standard errors for parameters (MLE/MAP)
- ihessian : holds any estimated inverse Hessian (MLE/MAP)
- chains : holds trace information for MCMC runs
- supported_methods : which inference methods are supported
- default_method : default inference method
Class Methods¶
adjust_prior(index,prior)
Adjusts a prior with the given parameter index. Arguments are:
- index : taking a value in range(0,no of parameters)
- prior : one of the prior objects listed in the Bayesian Inference section
1 2 | model.list_priors()
model.adjust_prior(2,ifr.Normal(0,1))
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fit(method)
Fits hyperparameters for the GP-NAR model. Arguments are:
- method : one of [‘BBVI’,MLE’,’MAP’,’M-H’,’Laplace’]
- printed : (default: True) whether to print output
- nsims : (default: 100000) how many simulations if M-H is chosen
- cov_matrix (default: None) covariance matrix for M-H
- iterations : (default: 30000) how many iterations if BBVI is chosen
- step : (default: 0.001) step size for BBVI
1 | model.fit("M-H",nsims=20000)
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list_priors()
Lists the current prior specification.
plot_fit()
Graphs the fit of the model.
predict(h)
Predicts h timesteps ahead. Arguments are:
- h : (default: 5) how many timesteps to predict ahead
- past_values : (default: 20) how many past observations to plot
- intervals : (default: True) whether to plot prediction intervals
1 | model.predict(h=12,past_values=36)
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