GPNARX models¶
Example¶
1 2 3 4 5 6  import numpy as np
import pandas as pd
import pyflux as pf
USgrowth = #somequarterlyGDPgrowthdatahere
model = pf.GPNARX(USgrowth, ar=4, kernel_type='OU')

Class Arguments¶

class
GPNARX
(data, ar, kernel_type, integ, target)¶ 
data
¶ pd.DataFrame or arraylike : the timeseries data

ar
¶ int : the number of autoregressive terms

kernel_type
¶ string : the type of kernel; one of [‘SE’,’RQ’,’OU’,’Periodic’,’ARD’]

integ
¶ int : Specifies how many time to difference the time series.

target
¶ string (data is DataFrame) or int (data is np.array) : which column to use as the time series. If None, the first column will be chosen as the data.

Class Methods¶

adjust_prior
(index, prior)¶ Adjusts the priors of the model. index can be an int or a list. prior is a prior object, such as Normal(0,3).
Here is example usage for adjust_prior()
:
1 2 3 4 5  import pyflux as pf
# model = ... (specify a model)
model.list_priors()
model.adjust_prior(2,pf.Normal(0,1))


fit
(method, **kwargs)¶ Estimates latent variables for the model. Returns a Results object. method is an inference/estimation option; see Bayesian Inference and Classical Inference sections for options. If no method is provided then a default will be used.
Optional arguments are specific to the method you choose  see the documentation for these methods for more detail.
Here is example usage for fit()
:
1 2 3 4  import pyflux as pf
# model = ... (specify a model)
model.fit("MH",nsims=20000)


plot_fit
(**kwargs)¶ Graphs the fit of the model.
Optional arguments include figsize  the dimensions of the figure to plot.

plot_z
(indices, figsize)¶ Returns a plot of the latent variables and their associated uncertainty. indices is a list referring to the latent variable indices that you want ot plot. Figsize specifies how big the plot will be.

plot_predict
(h, past_values, intervals, **kwargs)¶ Plots predictions of the model. h is an int of how many steps ahead to predict. past_values is an int of how many past values of the series to plot. intervals is a bool on whether to include confidence/credibility intervals or not.
Optional arguments include figsize  the dimensions of the figure to plot.

plot_predict_is
(h, fit_once, **kwargs)¶ Plots insample rolling predictions for the model. h is an int of how many previous steps to simulate performance on. fit_once is a boolean specifying whether to fit the model once at the beginning of the period (True), or whether to fit after every step (False).
Optional arguments include figsize  the dimensions of the figure to plot.

predict
(h)¶ Returns DataFrame of model predictions. h is an int of how many steps ahead to predict.

predict_is
(h, fit_once)¶ Returns DataFrame of insample rolling predictions for the model. h is an int of how many previous steps to simulate performance on. fit_once is a boolean specifying whether to fit the model once at the beginning of the period (True), or whether to fit after every step (False).