#!/usr/bin/env python
# coding: utf-8

# DO NOT EDIT
# Autogenerated from the notebook statespace_sarimax_internet.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT

# # SARIMAX: Model selection, missing data

# The example mirrors Durbin and Koopman (2012), Chapter 8.4 in
# application of Box-Jenkins methodology to fit ARMA models. The novel
# feature is the ability of the model to work on datasets with missing
# values.

import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt

import requests
from io import BytesIO
from zipfile import ZipFile

# Download the dataset
dk = requests.get('http://www.ssfpack.com/files/DK-data.zip').content
f = BytesIO(dk)
zipped = ZipFile(f)
df = pd.read_table(BytesIO(zipped.read('internet.dat')),
                   skiprows=1,
                   header=None,
                   sep='\s+',
                   engine='python',
                   names=['internet', 'dinternet'])

# ### Model Selection
#
# As in Durbin and Koopman, we force a number of the values to be missing.

# Get the basic series
dta_full = df.dinternet[1:].values
dta_miss = dta_full.copy()

# Remove datapoints
missing = np.r_[6, 16, 26, 36, 46, 56, 66, 72, 73, 74, 75, 76, 86, 96] - 1
dta_miss[missing] = np.nan

# Then we can consider model selection using the Akaike information
# criteria (AIC), but running the model for each variant and selecting the
# model with the lowest AIC value.
#
# There are a couple of things to note here:
#
# - When running such a large batch of models, particularly when the
# autoregressive and moving average orders become large, there is the
# possibility of poor maximum likelihood convergence. Below we ignore the
# warnings since this example is illustrative.
# - We use the option `enforce_invertibility=False`, which allows the
# moving average polynomial to be non-invertible, so that more of the models
# are estimable.
# - Several of the models do not produce good results, and their AIC value
# is set to NaN. This is not surprising, as Durbin and Koopman note
# numerical problems with the high order models.

import warnings

aic_full = pd.DataFrame(np.zeros((6, 6), dtype=float))
aic_miss = pd.DataFrame(np.zeros((6, 6), dtype=float))

warnings.simplefilter('ignore')

# Iterate over all ARMA(p,q) models with p,q in [0,6]
for p in range(6):
    for q in range(6):
        if p == 0 and q == 0:
            continue

        # Estimate the model with no missing datapoints
        mod = sm.tsa.statespace.SARIMAX(dta_full,
                                        order=(p, 0, q),
                                        enforce_invertibility=False)
        try:
            res = mod.fit(disp=False)
            aic_full.iloc[p, q] = res.aic
        except:
            aic_full.iloc[p, q] = np.nan

        # Estimate the model with missing datapoints
        mod = sm.tsa.statespace.SARIMAX(dta_miss,
                                        order=(p, 0, q),
                                        enforce_invertibility=False)
        try:
            res = mod.fit(disp=False)
            aic_miss.iloc[p, q] = res.aic
        except:
            aic_miss.iloc[p, q] = np.nan

# For the models estimated over the full (non-missing) dataset, the AIC
# chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1)
# specification is better due to parsimony.
#
# $$
# \text{Replication of:}\\
# \textbf{Table 8.1} ~~ \text{AIC for different ARMA models.}\\
# \newcommand{\r}[1]{{\color{red}{#1}}}
# \begin{array}{lrrrrrr}
# \hline
# q &      0 &      1 &      2 &      3 &      4 &      5 \\
# \hline
# p &     {} &     {} &     {} &     {} &     {} &     {} \\
# 0 &   0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\
# 1 & 529.24 & \r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\
# 2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\
# 3 & \r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\
# 4 & 513.93 & 512.89 &    nan &    nan & 514.81 & 516.08 \\
# 5 & 515.86 & 517.64 &    nan &    nan &    nan &    nan \\
# \hline
# \end{array}
# $$
#
# For the models estimated over missing dataset, the AIC chooses ARMA(1,1)
#
# $$
# \text{Replication of:}\\
# \textbf{Table 8.2} ~~ \text{AIC for different ARMA models with missing
# observations.}\\
# \begin{array}{lrrrrrr}
# \hline
# q &      0 &      1 &      2 &      3 &      4 &      5 \\
# \hline
# p &     {} &     {} &     {} &     {} &     {} &     {} \\
# 0 &   0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\
# 1 & 468.01 & \r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\
# 2 & 469.68 &    nan & 460.48 & 459.43 & 459.23 & 460.47 \\
# 3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\
# 4 & 469.00 & 459.52 &    nan & 463.04 & 459.35 & 460.96 \\
# 5 & 471.32 & 461.26 &    nan &    nan & 461.00 & 462.97 \\
# \hline
# \end{array}
# $$
#
# **Note**: the AIC values are calculated differently than in Durbin and
# Koopman, but show overall similar trends.

# ### Postestimation
#
# Using the ARMA(1,1) specification selected above, we perform in-sample
# prediction and out-of-sample forecasting.

# Statespace
mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1, 0, 1))
res = mod.fit(disp=False)
print(res.summary())

# In-sample one-step-ahead predictions, and out-of-sample forecasts
nforecast = 20
predict = res.get_prediction(end=mod.nobs + nforecast)
idx = np.arange(len(predict.predicted_mean))
predict_ci = predict.conf_int(alpha=0.5)

# Graph
fig, ax = plt.subplots(figsize=(12, 6))
ax.xaxis.grid()
ax.plot(dta_miss, 'k.')

# Plot
ax.plot(idx[:-nforecast], predict.predicted_mean[:-nforecast], 'gray')
ax.plot(idx[-nforecast:],
        predict.predicted_mean[-nforecast:],
        'k--',
        linestyle='--',
        linewidth=2)
ax.fill_between(idx, predict_ci[:, 0], predict_ci[:, 1], alpha=0.15)

ax.set(title='Figure 8.9 - Internet series')
