Gaussian State Space models¶
Example¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14  import numpy as np
import pyflux as pf
import pandas as pd
nile = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/datasets/Nile.csv')
nile.index = pd.to_datetime(nile['time'].values,format='%Y')
model = pf.LLEV(data=niles,target='Poisson') # local level
USgrowth = pd.DataFrame(np.log(growthdata['VALUE']))
USgrowth.index = pd.to_datetime(growthdata['DATE'])
USgrowth.columns = ['Logged US Real GDP']
model2 = pf.LLT(data=USgrowth) # local linear trend model

Class Arguments¶
The local level (LLEV) and local linear trend (LLT) models are of the following form:

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

integ
¶ int : how many times to difference the time series (default: 0)

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
LLT
(data, integ, target)¶ 
data
¶ pd.DataFrame or arraylike : the timeseries data

integ
¶ int : how many times to difference the time series (default: 0)

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.

The dynamic linear regression (DynReg) model is of the form:
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 parameters 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
(intervals, **kwargs)¶ Graphs the fit of the model. intervals is a boolean; if true shows 95% C.I. intervals for the states.
Optional arguments include figsize  the dimensions of the figure to plot  and series_type which has two options: Filtered or Smoothed.

plot_parameters
(indices, figsize)¶ Returns a plot of the parameters and their associated uncertainty. indices is a list referring to the parameter 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, past_values, intervals, **kwargs)¶ Plots insample rolling predictions for the model. h is an int of how many previous steps to simulate performance on. 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.

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

predict_is
(h)¶ Returns DataFrame of insample rolling predictions for the model. h is an int of how many previous steps to simulate performance on.

simulation_smoother
(data, beta)¶ Outputs a simulated state trajectory from a simulation smoother. Arguments are data : the data to simulate from  use self.data usually  and beta : the parameters to use.