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# Gaussian Local Linear Trend models

## Introduction

Gaussian state space models - often called structural time series or unobserved component models - provide a way to decompose a time series into several distinct components. These components can be extracted in closed form using the Kalman filter if the errors are jointly Gaussian, and parameters can be estimated via the prediction error decomposition and Maximum Likelihood.

One classic univariate structural time series model is the local linear trend model. We can write this as a combination of a time-varying level, time-varying trend and an irregular term:

y_{t} = \mu_{t} + \epsilon_{t}

\mu_{t} =  \beta_{t-1} + \mu_{t-1} + \eta_{t}

\beta_{t} =  \beta_{t-1} + \zeta_{t}

\epsilon_{t} \sim N\left(0,\sigma_{\epsilon}^{2}\right)

\eta_{t} \sim N\left(0,\sigma_{\eta}^{2}\right)

\zeta_{t} \sim N\left(0,\sigma_{\zeta}^{2}\right)


## Example

We will use data on logged US Real GDP.

growthdata = pd.read_csv('http://www.pyflux.com/notebooks/GDPC1.csv')
USgrowth = pd.DataFrame(np.log(growthdata['VALUE']))
USgrowth.index = pd.to_datetime(growthdata['DATE'])
USgrowth.columns = ['Logged US Real GDP']
plt.figure(figsize=(15,5))
plt.plot(USgrowth.index, USgrowth)
plt.ylabel('Real GDP')
plt.title('US Real GDP');

Here define a Local Linear Trend model as follows:

model = pf.LLT(data=USgrowth)

We can also use the higher-level wrapper which allows us to specify the family, although if we pick a non-Gaussian family then the model will be estimated in a different way (not through the Kalman filter):

model = pf.LocalTrend(data=USgrowth, family=pf.Normal())

Next we estimate the latent variables. For this example we will use a maximum likelihood point mass estimate z^{MLE}:

x = model.fit()
x.summary()

LLT
======================================== =================================================
Dependent Variable: Logged US Real GDP   Method: MLE
Start Date: 1947-01-01 00:00:00          Log Likelihood: 877.3334
End Date: 2015-10-01 00:00:00            AIC: -1748.6667
Number of observations: 276              BIC: -1737.8055
==========================================================================================
Latent Variable           Estimate   Std Error  z        P>|z|    95% C.I.
========================= ========== ========== ======== ======== ========================
Sigma^2 irregular         3.306e-07
Sigma^2 level             5.41832e-0
Sigma^2 trend             1.55224e-0
==========================================================================================

We can plot the in-sample fit using :py:func:plot_fit:

model.plot_fit(figsize=(15,10))

The trend picks out the underlying local growth rate in the series. Together with the local level this constitutes the smoothed series. We can use the simulation smoother to simulate draws from the states, using py:func:simulation_smoother:. We draw from the local trend state:

plt.figure(figsize=(15,5))
for i in range(10):
plt.plot(model.index,model.simulation_smoother(
model.latent_variables.get_z_values())[1][0:model.index.shape[0]])
plt.show()

If we want to plot rolling in-sample predictions, we can use the :py:func:plot_predict_is: method:

model.plot_predict_is(h=15,figsize=(15,5))

We can view out-of-sample predictions using :py:func:plot_predict:

model.plot_predict(h=20, past_values=17*4, figsize=(15,6))

If we want the predictions in a DataFrame form, then we can just use the :py:func:predict: method.

## Class Description

.. py:class:: LLT(data, integ, target)

**Local Linear Trend Models.**

==================   ===============================    ======================================
Parameter            Type                                Description
==================   ===============================    ======================================
data                 pd.DataFrame or np.ndarray         Contains the univariate time series
integ                int                                How many times to difference the data
(default: 0)
target               string or int                      Which column of DataFrame/array to use.
==================   ===============================    ======================================

**Attributes**

.. py:attribute:: latent_variables

A pf.LatentVariables() object containing information on the model latent variables,
prior settings. any fitted values, starting values, and other latent variable
information. When a model is fitted, this is where the latent variables are updated/stored.
Please see the documentation on Latent Variables for information on attributes within this
object, as well as methods for accessing the latent variable information.

**Methods**

Adjusts the priors for the model latent variables. The latent variables and their indices
can be viewed by printing the latent_variables attribute attached to the model instance.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
index                int                         Index of the latent variable to change
prior                pf.Family instance          Prior distribution, e.g. pf.Normal()
==================   ========================    ======================================

**Returns**: void - changes the model latent_variables attribute

.. py:method:: fit(method, **kwargs)

Estimates latent variables for the model. User chooses an inference option and the
method returns a results object, as well as updating the model's latent_variables
attribute.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
method               str                         Inference option: e.g. 'M-H' or 'MLE'
==================   ========================    ======================================

See Bayesian Inference and Classical Inference sections of the documentation for the
full list of inference options. Optional parameters can be entered that are relevant
to the particular mode of inference chosen.

**Returns**: pf.Results instance with information for the estimated latent variables

.. py:method:: plot_fit(**kwargs)

Plots the fit of the model against the data. Optional arguments include *figsize*,
the dimensions of the figure to plot.

**Returns** : void - shows a matplotlib plot

.. py:method:: plot_ppc(T, nsims)

Plots a histogram for a posterior predictive check with a discrepancy measure of the
user's choosing. This method only works if you have fitted using Bayesian inference.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
T                    function                    Discrepancy, e.g. np.mean or np.max
nsims                int                         How many simulations for the PPC
==================   ========================    ======================================

**Returns**: void - shows a matplotlib plot

.. py:method:: plot_predict(h, past_values, intervals, **kwargs)

Plots predictions of the model, along with intervals.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
h                    int                         How many steps to forecast ahead
past_values          int                         How many past datapoints to plot
intervals            boolean                     Whether to plot intervals or not
==================   ========================    ======================================

Optional arguments include *figsize* - the dimensions of the figure to plot. Please note
that if you use Maximum Likelihood or Variational Inference, the intervals shown will not
reflect latent variable uncertainty. Only Metropolis-Hastings will give you fully Bayesian
prediction intervals. Bayesian intervals with variational inference are not shown because
of the limitation of mean-field inference in not accounting for posterior correlations.

**Returns** : void - shows a matplotlib plot

.. py:method:: plot_predict_is(h, fit_once, fit_method, **kwargs)

Plots in-sample rolling predictions for the model. This means that the user pretends a
last subsection of data is out-of-sample, and forecasts after each period and assesses
how well they did. The user can choose whether to fit parameters once at the beginning
or every time step.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
h                    int                         How many previous timesteps to use
fit_once             boolean                     Whether to fit once, or every timestep
fit_method           str                         Which inference option, e.g. 'MLE'
==================   ========================    ======================================

Optional arguments include *figsize* - the dimensions of the figure to plot. **h** is an int of how many previous steps to simulate performance on.

**Returns** : void - shows a matplotlib plot

.. py:method:: plot_sample(nsims, plot_data=True)

Plots samples from the posterior predictive density of the model. This method only works
if you fitted the model using Bayesian inference.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
nsims                int                         How many samples to draw
plot_data            boolean                     Whether to plot the real data as well
==================   ========================    ======================================

**Returns** : void - shows a matplotlib plot

.. py:method:: plot_z(indices, figsize)

Returns a plot of the latent variables and their associated uncertainty.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
indices              int or list                 Which latent variable indices to plot
figsize              tuple                       Size of the matplotlib figure
==================   ========================    ======================================

**Returns** : void - shows a matplotlib plot

.. py:method:: ppc(T, nsims)

Returns a p-value for a posterior predictive check. This method only works if you have
fitted using Bayesian inference.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
T                    function                    Discrepancy, e.g. np.mean or np.max
nsims                int                         How many simulations for the PPC
==================   ========================    ======================================

**Returns**: int - the p-value for the discrepancy test

.. py:method:: predict(h, intervals=False)

Returns a DataFrame of model predictions.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
h                    int                         How many steps to forecast ahead
intervals            boolean                     Whether to return prediction intervals
==================   ========================    ======================================

Please note that if you use Maximum Likelihood or Variational Inference, the intervals shown
will not reflect latent variable uncertainty. Only Metropolis-Hastings will give you fully
Bayesian prediction intervals. Bayesian intervals with variational inference are not shown
because of the limitation of mean-field inference in not accounting for posterior correlations.

**Returns** : pd.DataFrame - the model predictions

.. py:method:: predict_is(h, fit_once, fit_method)

Returns DataFrame of in-sample rolling predictions for the model.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
h                    int                         How many previous timesteps to use
fit_once             boolean                     Whether to fit once, or every timestep
fit_method           str                         Which inference option, e.g. 'MLE'
==================   ========================    ======================================

**Returns** : pd.DataFrame - the model predictions

.. py:method:: sample(nsims)

Returns np.ndarray of draws of the data from the posterior predictive density. This
method only works if you have fitted the model using Bayesian inference.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
nsims                int                         How many posterior draws to take
==================   ========================    ======================================

**Returns** : np.ndarray - samples from the posterior predictive density.

.. py:method:: simulation_smoother(beta)

Returns np.ndarray of draws of the data from the Durbin and Koopman (2002) simulation smoother.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
beta                 np.array                    np.array of latent variables
==================   ========================    ======================================

Recommended just to use model.latent_variables.get_z_values() for the beta input, if you