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# ARIMAX models

## Introduction

Autoregressive integrated moving average (ARIMAX) models extend ARIMA models through the inclusion of exogenous variables X. We write an ARIMAX(p,d,q) model for some time series data y_{t} and exogenous data X_{t}, where p is the number of autoregressive lags, d is the degree of differencing and q is the number of moving average lags as:

\Delta^{D}y_{t} = \sum^{p}_{i=1}\phi_{i}\Delta^{D}y_{t-i} + \sum^{q}_{j=1}\theta_{j}\epsilon_{t-j} + \sum^{M}_{m=1}\beta_{m}X{m,t} + \epsilon_{t}

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


## Example

We will combine ARIMA dynamics with intervention analysis for monthly UK driver death data. There are two interventions we are interested in: the 1974 oil crisis and the introduction of the seatbelt law in 1983. We will model the effects of these events as structural breaks.

import numpy as np
import pandas as pd
import pyflux as pf
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline

data.index = data['time'];
data.loc[(data['time']>=1983.05), 'seat_belt'] = 1;
data.loc[(data['time']<1983.05), 'seat_belt'] = 0;
data.loc[(data['time']>=1974.00), 'oil_crisis'] = 1;
data.loc[(data['time']<1974.00), 'oil_crisis'] = 0;
plt.figure(figsize=(15,5));
plt.plot(data.index,data['drivers']);
plt.ylabel('Driver Deaths');
plt.title('Deaths of Car Drivers in Great Britain 1969-84');
plt.plot();

The structural breaks can be included via patsy notation. Below we estimate a point mass estimate z^{MLE} of the latent variables:

model = pf.ARIMAX(data=data, formula='drivers~1+seat_belt+oil_crisis',
ar=1, ma=1, family=pf.Normal())
x = model.fit("MLE")
x.summary()

ARIMAX(1,0,1)
======================================== ================================================
Dependent Variable: drivers              Method: MLE
Start Date: 1969.08333333                Log Likelihood: -1278.7616
End Date: 1984.91666667                  AIC: 2569.5232
Number of observations: 191              BIC: 2589.0368
=========================================================================================
Latent Variable          Estimate   Std Error  z        P>|z|    95% C.I.
======================== ========== ========== ======== ======== ========================
AR(1)                    0.5002     0.0933     5.3607   0.0      (0.3173 | 0.6831)
MA(1)                    0.1713     0.0991     1.7275   0.0841   (-0.023 | 0.3656)
Beta 1                   946.585    176.9439   5.3496   0.0      (599.7749 | 1293.3952)
Beta seat_belt           -57.8924   57.8211    -1.0012  0.3167   (-171.2217 | 55.437)
Beta oil_crisis          -151.673   44.119     -3.4378  0.0006   (-238.1462 | -65.1998)
Sigma                    195.6042
=========================================================================================

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

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

To forecast forward we need exogenous variables for future dates. Since the interventions carry forward, we can just use a slice of the existing dataframe and use func:plot_predict:

model.plot_predict(h=10, oos_data=data.iloc[-12:], past_values=100, figsize=(15,5))

## Class Description

.. py:class:: ARIMAX(data, formula, ar, ma, integ, target, family)

**Autoregressive Integrated Moving Average Exogenous Variable Models (ARIMAX).**

==================   ===============================    ======================================
Parameter            Type                                Description
==================   ===============================    ======================================
data                 pd.DataFrame or np.ndarray         Contains the univariate time series
formula              string                             Patsy notation specifying the regression
ar                   int                                The number of autoregressive lags
ma                   int                                The number of moving average lags
integ                int                                How many times to difference the data
(default: 0)
target               string or int                      Which column of DataFrame/array to use.
family               pf.Family instance                 The distribution for the time series,
e.g pf.Normal()
==================   ===============================    ======================================

**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, oos_data, past_values, intervals, **kwargs)

Plots predictions of the model, along with intervals.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
h                    int                         How many steps to forecast ahead
oos_data             pd.DataFrame                Exogenous variables in a frame for h steps
past_values          int                         How many past datapoints to plot
intervals            boolean                     Whether to plot intervals or not
==================   ========================    ======================================

To be clear, the *oos_data* argument should be a DataFrame in the same format as the initial
dataframe used to initialize the model instance. The reason is that to predict future values,
you need to specify assumptions about exogenous variables for the future. For example, if you
predict *h* steps ahead, the method will take the h first rows from *oos_data* and take the
values for the exogenous variables that you asked for in the patsy formula.

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, oos_data, intervals=False)

Returns a DataFrame of model predictions.

==================   ========================    ======================================
Parameter            Type                        Description
==================   ========================    ======================================
h                    int                         How many steps to forecast ahead
oos_data             pd.DataFrame                Exogenous variables in a frame for h steps
intervals            boolean                     Whether to return prediction intervals
==================   ========================    ======================================

To be clear, the *oos_data* argument should be a DataFrame in the same format as the initial
dataframe used to initialize the model instance. The reason is that to predict future values,
you need to specify assumptions about exogenous variables for the future. For example, if you
predict *h* steps ahead, the method will take the 5 first rows from *oos_data* and take the
values for the exogenous variables that you specified as exogenous variables in the patsy formula.

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.



## References

Box, G; Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.

Hamilton, J.D. (1994). Time Series Analysis. Taylor & Francis US.

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