GP-NARX models

Example

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 array-like : the time-series 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():

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():

import pyflux as pf

# model = ... (specify a model)
model.fit("M-H",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 in-sample 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 in-sample 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).