diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..894a44c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,104 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
diff --git a/Forecasting a Time Series in Python.ipynb b/Forecasting a Time Series in Python.ipynb
deleted file mode 100644
index e082305..0000000
--- a/Forecasting a Time Series in Python.ipynb	
+++ /dev/null
@@ -1,1265 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to Forecast a Time Series with Python\n",
-    "\n",
-    "Wouldn't it be nice to know the future? This is the notebook that relates to the blog post on medium. Please check the blog for visualizations and explanations, this notebook is really just for the code :)\n",
-    "\n",
-    "\n",
-    "## Processing the Data\n",
-    "\n",
-    "Let's explore the Industrial production of electric and gas utilities in the United States, from the years 1985-2018, with our frequency being Monthly production output.\n",
-    "\n",
-    "You can access this data here: https://fred.stlouisfed.org/series/IPG2211A2N\n",
-    "\n",
-    "This data measures the real output of all relevant establishments located in the United States, regardless of their ownership, but not those located in U.S. territories."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>IPG2211A2N</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1985-01-01</th>\n",
-       "      <td>72.5052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-02-01</th>\n",
-       "      <td>70.6720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-03-01</th>\n",
-       "      <td>62.4502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-04-01</th>\n",
-       "      <td>57.4714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-05-01</th>\n",
-       "      <td>55.3151</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            IPG2211A2N\n",
-       "DATE                  \n",
-       "1985-01-01     72.5052\n",
-       "1985-02-01     70.6720\n",
-       "1985-03-01     62.4502\n",
-       "1985-04-01     57.4714\n",
-       "1985-05-01     55.3151"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import pandas as pd\n",
-    "data = pd.read_csv(\"Electric_Production.csv\",index_col=0)\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Right now our index is actually just a list of strings that look like a date, we'll want to adjust these to be timestamps, that way our forecasting analysis will be able to interpret these values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['1985-01-01', '1985-02-01', '1985-03-01', '1985-04-01', '1985-05-01',\n",
-       "       '1985-06-01', '1985-07-01', '1985-08-01', '1985-09-01', '1985-10-01',\n",
-       "       ...\n",
-       "       '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01', '2017-08-01',\n",
-       "       '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01', '2018-01-01'],\n",
-       "      dtype='object', name='DATE', length=397)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data.index = pd.to_datetime(data.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>IPG2211A2N</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1985-01-01</th>\n",
-       "      <td>72.5052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-02-01</th>\n",
-       "      <td>70.6720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-03-01</th>\n",
-       "      <td>62.4502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-04-01</th>\n",
-       "      <td>57.4714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-05-01</th>\n",
-       "      <td>55.3151</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            IPG2211A2N\n",
-       "DATE                  \n",
-       "1985-01-01     72.5052\n",
-       "1985-02-01     70.6720\n",
-       "1985-03-01     62.4502\n",
-       "1985-04-01     57.4714\n",
-       "1985-05-01     55.3151"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatetimeIndex(['1985-01-01', '1985-02-01', '1985-03-01', '1985-04-01',\n",
-       "               '1985-05-01', '1985-06-01', '1985-07-01', '1985-08-01',\n",
-       "               '1985-09-01', '1985-10-01',\n",
-       "               ...\n",
-       "               '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01',\n",
-       "               '2017-08-01', '2017-09-01', '2017-10-01', '2017-11-01',\n",
-       "               '2017-12-01', '2018-01-01'],\n",
-       "              dtype='datetime64[ns]', name='DATE', length=397, freq=None)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's first make sure that the data doesn't have any missing data points:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>IPG2211A2N</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [IPG2211A2N]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[pd.isnull(data['IPG2211A2N'])]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also rename this column since its hard to remember what \"IPG2211A2N\" code stands for:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data.columns = ['Energy Production']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energy Production</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1985-01-01</th>\n",
-       "      <td>72.5052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-02-01</th>\n",
-       "      <td>70.6720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-03-01</th>\n",
-       "      <td>62.4502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-04-01</th>\n",
-       "      <td>57.4714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-05-01</th>\n",
-       "      <td>55.3151</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Energy Production\n",
-       "DATE                         \n",
-       "1985-01-01            72.5052\n",
-       "1985-02-01            70.6720\n",
-       "1985-03-01            62.4502\n",
-       "1985-04-01            57.4714\n",
-       "1985-05-01            55.3151"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import plotly\n",
-    "# plotly.tools.set_credentials_file()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://plot.ly/~Pierian-Data/12'"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from plotly.plotly import plot_mpl\n",
-    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
-    "result = seasonal_decompose(data, model='multiplicative')\n",
-    "fig = result.plot()\n",
-    "plot_mpl(fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import plotly.plotly as ply\n",
-    "import cufflinks as cf\n",
-    "# Check the docs on setting up offline plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.iplot(title=\"Energy Production Jan 1985--Jan 2018\", theme='pearl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Marcial\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning:\n",
-      "\n",
-      "The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pyramid.arima import auto_arima"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**he AIC measures how well a model fits the data while taking into account the overall complexity of the model. A model that fits the data very well while using lots of features will be assigned a larger AIC score than a model that uses fewer features to achieve the same goodness-of-fit. Therefore, we are interested in finding the model that yields the lowest AIC value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=1782.527, BIC=1802.447, Fit time=0.866 seconds\n",
-      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 1, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
-      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 0, 12); AIC=1942.040, BIC=1957.976, Fit time=0.238 seconds\n",
-      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=1837.289, BIC=1853.224, Fit time=0.361 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=1783.875, BIC=1807.778, Fit time=1.434 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 0, 12); AIC=1920.884, BIC=1936.820, Fit time=0.451 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=1784.212, BIC=1808.116, Fit time=3.491 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1781.900, BIC=1809.788, Fit time=3.586 seconds\n",
-      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1837.164, BIC=1861.067, Fit time=1.287 seconds\n",
-      "Fit ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1782.648, BIC=1814.520, Fit time=4.348 seconds\n",
-      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=1852.587, BIC=1876.490, Fit time=1.008 seconds\n",
-      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=1781.940, BIC=1813.811, Fit time=4.222 seconds\n",
-      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=1864.184, BIC=1884.103, Fit time=1.035 seconds\n",
-      "Fit ARIMA: order=(2, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=1782.722, BIC=1818.578, Fit time=5.144 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1772.539, BIC=1804.410, Fit time=4.551 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=1771.295, BIC=1799.182, Fit time=2.270 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 0, 12); AIC=1870.049, BIC=1889.969, Fit time=0.974 seconds\n",
-      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=1825.210, BIC=1849.114, Fit time=0.999 seconds\n",
-      "Fit ARIMA: order=(2, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=1772.010, BIC=1803.881, Fit time=3.391 seconds\n",
-      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(2, 1, 1, 12); AIC=1842.551, BIC=1866.454, Fit time=0.808 seconds\n",
-      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(2, 1, 1, 12); AIC=1771.612, BIC=1803.484, Fit time=4.359 seconds\n",
-      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(2, 1, 1, 12); AIC=1855.606, BIC=1875.526, Fit time=0.641 seconds\n",
-      "Fit ARIMA: order=(2, 1, 2) seasonal_order=(2, 1, 1, 12); AIC=1773.049, BIC=1808.904, Fit time=5.352 seconds\n",
-      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 0, 12); AIC=1813.388, BIC=1837.291, Fit time=3.069 seconds\n",
-      "Total fit time: 53.889 seconds\n"
-     ]
-    }
-   ],
-   "source": [
-    "stepwise_model = auto_arima(data, start_p=1, start_q=1,\n",
-    "                           max_p=3, max_q=3, m=12,\n",
-    "                           start_P=0, seasonal=True,\n",
-    "                           d=1, D=1, trace=True,\n",
-    "                           error_action='ignore',  \n",
-    "                           suppress_warnings=True, \n",
-    "                           stepwise=True) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1771.2948217037836"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stepwise_model.aic()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train Test Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energy Production</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1985-01-01</th>\n",
-       "      <td>72.5052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-02-01</th>\n",
-       "      <td>70.6720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-03-01</th>\n",
-       "      <td>62.4502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-04-01</th>\n",
-       "      <td>57.4714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-05-01</th>\n",
-       "      <td>55.3151</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Energy Production\n",
-       "DATE                         \n",
-       "1985-01-01            72.5052\n",
-       "1985-02-01            70.6720\n",
-       "1985-03-01            62.4502\n",
-       "1985-04-01            57.4714\n",
-       "1985-05-01            55.3151"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 397 entries, 1985-01-01 to 2018-01-01\n",
-      "Data columns (total 1 columns):\n",
-      "Energy Production    397 non-null float64\n",
-      "dtypes: float64(1)\n",
-      "memory usage: 26.2 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "data.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll train on 20 years of data, from the years 1985-2015 and test our forcast on the years after that and compare it to the real data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "train = data.loc['1985-01-01':'2016-12-01']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energy Production</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016-08-01</th>\n",
-       "      <td>115.5159</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-09-01</th>\n",
-       "      <td>102.7637</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-10-01</th>\n",
-       "      <td>91.4867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-11-01</th>\n",
-       "      <td>92.8900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-01</th>\n",
-       "      <td>112.7694</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Energy Production\n",
-       "DATE                         \n",
-       "2016-08-01           115.5159\n",
-       "2016-09-01           102.7637\n",
-       "2016-10-01            91.4867\n",
-       "2016-11-01            92.8900\n",
-       "2016-12-01           112.7694"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "test = data.loc['2015-01-01':]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energy Production</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2015-01-01</th>\n",
-       "      <td>120.2696</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-02-01</th>\n",
-       "      <td>116.3788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-03-01</th>\n",
-       "      <td>104.4706</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-04-01</th>\n",
-       "      <td>89.7461</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-05-01</th>\n",
-       "      <td>91.0930</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Energy Production\n",
-       "DATE                         \n",
-       "2015-01-01           120.2696\n",
-       "2015-02-01           116.3788\n",
-       "2015-03-01           104.4706\n",
-       "2015-04-01            89.7461\n",
-       "2015-05-01            91.0930"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energy Production</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2017-09-01</th>\n",
-       "      <td>98.6154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-10-01</th>\n",
-       "      <td>93.6137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-11-01</th>\n",
-       "      <td>97.3359</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-12-01</th>\n",
-       "      <td>114.7212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01</th>\n",
-       "      <td>129.4048</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Energy Production\n",
-       "DATE                         \n",
-       "2017-09-01            98.6154\n",
-       "2017-10-01            93.6137\n",
-       "2017-11-01            97.3359\n",
-       "2017-12-01           114.7212\n",
-       "2018-01-01           129.4048"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ARIMA(callback=None, disp=0, maxiter=50, method=None, order=(1, 1, 1),\n",
-       "   out_of_sample_size=0, scoring='mse', scoring_args={},\n",
-       "   seasonal_order=(2, 1, 1, 12), solver='lbfgs', start_params=None,\n",
-       "   suppress_warnings=True, transparams=True, trend='c')"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stepwise_model.fit(train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "future_forecast = stepwise_model.predict(n_periods=37)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 121.0069841 ,  110.05829773,  100.66214128,   90.6704282 ,\n",
-       "         92.16164339,  103.22814008,  112.50690673,  112.11184857,\n",
-       "        101.04339558,   92.07696292,   95.82735302,  111.26301564,\n",
-       "        120.21587944,  111.32815023,  102.16625906,   90.55576703,\n",
-       "         92.13024541,  102.88613935,  111.8729164 ,  111.06722082,\n",
-       "        100.84852305,   92.07136347,   95.82277211,  109.23003471,\n",
-       "        119.35147539,  110.56205836,  100.99051798,   90.20673887,\n",
-       "         91.75667578,  102.973546  ,  112.20965839,  111.68458911,\n",
-       "        101.10323951,   91.83416937,   95.08657978,  109.42245514,\n",
-       "        119.38303158])"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "future_forecast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "future_forecast = pd.DataFrame(future_forecast,index = test.index,columns=['Prediction'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Prediction</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2015-01-01</th>\n",
-       "      <td>121.006984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-02-01</th>\n",
-       "      <td>110.058298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-03-01</th>\n",
-       "      <td>100.662141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-04-01</th>\n",
-       "      <td>90.670428</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-05-01</th>\n",
-       "      <td>92.161643</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Prediction\n",
-       "DATE                  \n",
-       "2015-01-01  121.006984\n",
-       "2015-02-01  110.058298\n",
-       "2015-03-01  100.662141\n",
-       "2015-04-01   90.670428\n",
-       "2015-05-01   92.161643"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "future_forecast.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energy Production</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DATE</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2015-01-01</th>\n",
-       "      <td>120.2696</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-02-01</th>\n",
-       "      <td>116.3788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-03-01</th>\n",
-       "      <td>104.4706</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-04-01</th>\n",
-       "      <td>89.7461</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-05-01</th>\n",
-       "      <td>91.0930</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Energy Production\n",
-       "DATE                         \n",
-       "2015-01-01           120.2696\n",
-       "2015-02-01           116.3788\n",
-       "2015-03-01           104.4706\n",
-       "2015-04-01            89.7461\n",
-       "2015-05-01            91.0930"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~Pierian-Data/16.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([test,future_forecast],axis=1).iplot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "future_forecast2 = future_forcast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~Pierian-Data/18.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([data,future_forecast2],axis=1).iplot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/Electric_Production.csv b/data/Electric_Production.csv
similarity index 100%
rename from Electric_Production.csv
rename to data/Electric_Production.csv
diff --git a/forecasting_time_series_python.ipynb b/forecasting_time_series_python.ipynb
new file mode 100644
index 0000000..0507731
--- /dev/null
+++ b/forecasting_time_series_python.ipynb
@@ -0,0 +1,1311 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to Forecast a Time Series with Python\n",
+    "\n",
+    "Wouldn't it be nice to know the future? This is the notebook that relates to the blog post on medium. Please check the blog for visualizations and explanations, this notebook is really just for the code :)\n",
+    "\n",
+    "\n",
+    "## Processing the Data\n",
+    "\n",
+    "Let's explore the Industrial production of electric and gas utilities in the United States, from the years 1985-2018, with our frequency being Monthly production output.\n",
+    "\n",
+    "You can access this data here: https://fred.stlouisfed.org/series/IPG2211A2N\n",
+    "\n",
+    "This data measures the real output of all relevant establishments located in the United States, regardless of their ownership, but not those located in U.S. territories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pmdarima import auto_arima\n",
+    "from plotly.offline import init_notebook_mode, iplot, plot_mpl\n",
+    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
+    "\n",
+    "\n",
+    "init_notebook_mode(connected=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IPG2211A2N</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1985-01-01</th>\n",
+       "      <td>72.5052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-02-01</th>\n",
+       "      <td>70.6720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-03-01</th>\n",
+       "      <td>62.4502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-04-01</th>\n",
+       "      <td>57.4714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-05-01</th>\n",
+       "      <td>55.3151</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            IPG2211A2N\n",
+       "DATE                  \n",
+       "1985-01-01     72.5052\n",
+       "1985-02-01     70.6720\n",
+       "1985-03-01     62.4502\n",
+       "1985-04-01     57.4714\n",
+       "1985-05-01     55.3151"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"data/Electric_Production.csv\", index_col=0)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Right now our index is actually just a list of strings that look like a date, we'll want to adjust these to be timestamps, that way our forecasting analysis will be able to interpret these values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['1985-01-01', '1985-02-01', '1985-03-01', '1985-04-01', '1985-05-01',\n",
+       "       '1985-06-01', '1985-07-01', '1985-08-01', '1985-09-01', '1985-10-01',\n",
+       "       ...\n",
+       "       '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01', '2017-08-01',\n",
+       "       '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01', '2018-01-01'],\n",
+       "      dtype='object', name='DATE', length=397)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.index = pd.to_datetime(data.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IPG2211A2N</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1985-01-01</th>\n",
+       "      <td>72.5052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-02-01</th>\n",
+       "      <td>70.6720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-03-01</th>\n",
+       "      <td>62.4502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-04-01</th>\n",
+       "      <td>57.4714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-05-01</th>\n",
+       "      <td>55.3151</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            IPG2211A2N\n",
+       "DATE                  \n",
+       "1985-01-01     72.5052\n",
+       "1985-02-01     70.6720\n",
+       "1985-03-01     62.4502\n",
+       "1985-04-01     57.4714\n",
+       "1985-05-01     55.3151"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['1985-01-01', '1985-02-01', '1985-03-01', '1985-04-01',\n",
+       "               '1985-05-01', '1985-06-01', '1985-07-01', '1985-08-01',\n",
+       "               '1985-09-01', '1985-10-01',\n",
+       "               ...\n",
+       "               '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01',\n",
+       "               '2017-08-01', '2017-09-01', '2017-10-01', '2017-11-01',\n",
+       "               '2017-12-01', '2018-01-01'],\n",
+       "              dtype='datetime64[ns]', name='DATE', length=397, freq=None)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first make sure that the data doesn't have any missing data points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IPG2211A2N</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [IPG2211A2N]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[pd.isnull(data['IPG2211A2N'])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's also rename this column since its hard to remember what \"IPG2211A2N\" code stands for:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.columns = ['Energy Production']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Energy Production</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1985-01-01</th>\n",
+       "      <td>72.5052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-02-01</th>\n",
+       "      <td>70.6720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-03-01</th>\n",
+       "      <td>62.4502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-04-01</th>\n",
+       "      <td>57.4714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-05-01</th>\n",
+       "      <td>55.3151</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Energy Production\n",
+       "DATE                         \n",
+       "1985-01-01            72.5052\n",
+       "1985-02-01            70.6720\n",
+       "1985-03-01            62.4502\n",
+       "1985-04-01            57.4714\n",
+       "1985-05-01            55.3151"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series Decomposition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# from plotly.plotly import plot_mpl\n",
+    "\n",
+    "y = seasonal_decompose(data, model='multiplicative')\n",
+    "\n",
+    "fig     = plt.figure(figsize=(12, 7))\n",
+    "layout  = (2, 2)\n",
+    "obs_ax  = plt.subplot2grid(layout, (0, 0))\n",
+    "ses_ax  = plt.subplot2grid(layout, (0, 1))\n",
+    "tnd_ax  = plt.subplot2grid(layout, (1, 0))\n",
+    "rsd_ax  = plt.subplot2grid(layout, (1, 1))\n",
+    "\n",
+    "y.observed.plot(ax=obs_ax, title = 'Observed')\n",
+    "y.trend.plot(ax=ses_ax, title = 'Trend')\n",
+    "y.seasonal.plot(ax=tnd_ax, title = 'Seasonal')\n",
+    "y.resid.plot(ax=rsd_ax, title = 'Residual')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x22988863978>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(title=\"Energy Production Jan 1985--Jan 2018\", figsize=(15,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**he AIC measures how well a model fits the data while taking into account the overall complexity of the model. A model that fits the data very well while using lots of features will be assigned a larger AIC score than a model that uses fewer features to achieve the same goodness-of-fit. Therefore, we are interested in finding the model that yields the lowest AIC value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=1782.527, BIC=1802.280, Fit time=1.363 seconds\n",
+      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 1, 0, 12); AIC=2012.117, BIC=2020.018, Fit time=0.026 seconds\n",
+      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 0, 12); AIC=1942.042, BIC=1957.844, Fit time=0.334 seconds\n",
+      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=1837.289, BIC=1853.091, Fit time=0.761 seconds\n",
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=1783.890, BIC=1807.594, Fit time=3.038 seconds\n",
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 0, 12); AIC=1920.884, BIC=1936.687, Fit time=0.460 seconds\n",
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=1783.389, BIC=1807.093, Fit time=5.975 seconds\n",
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1782.020, BIC=1809.675, Fit time=4.924 seconds\n",
+      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1837.164, BIC=1860.868, Fit time=1.880 seconds\n",
+      "Fit ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1782.782, BIC=1814.387, Fit time=7.627 seconds\n",
+      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=1852.587, BIC=1876.291, Fit time=2.490 seconds\n",
+      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=1781.937, BIC=1813.542, Fit time=9.291 seconds\n",
+      "Fit ARIMA: order=(2, 1, 3) seasonal_order=(1, 1, 2, 12); AIC=1785.928, BIC=1825.434, Fit time=11.308 seconds\n",
+      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(0, 1, 2, 12); AIC=1787.858, BIC=1815.513, Fit time=9.008 seconds\n",
+      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=1771.115, BIC=1806.671, Fit time=9.795 seconds\n",
+      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(2, 1, 1, 12); AIC=1771.602, BIC=1803.207, Fit time=8.829 seconds\n",
+      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(1, 1, 1, 12); AIC=1784.191, BIC=1811.846, Fit time=4.412 seconds\n",
+      "Fit ARIMA: order=(0, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=1779.256, BIC=1810.861, Fit time=5.513 seconds\n",
+      "Fit ARIMA: order=(2, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=1775.270, BIC=1814.776, Fit time=10.030 seconds\n",
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1770.202, BIC=1801.807, Fit time=8.065 seconds\n",
+      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(2, 1, 2, 12); AIC=1854.601, BIC=1878.305, Fit time=2.712 seconds\n",
+      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=1771.290, BIC=1798.945, Fit time=7.363 seconds\n",
+      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1816.627, BIC=1844.282, Fit time=3.733 seconds\n",
+      "Fit ARIMA: order=(2, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1772.009, BIC=1807.565, Fit time=9.559 seconds\n",
+      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(2, 1, 2, 12); AIC=1837.278, BIC=1864.933, Fit time=3.146 seconds\n",
+      "Total fit time: 131.651 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "stepwise_model = auto_arima(data, start_p=1, start_q=1,\n",
+    "                           max_p=3, max_q=3, m=12,\n",
+    "                           start_P=0, seasonal=True,\n",
+    "                           d=1, D=1, trace=True,\n",
+    "                           error_action='ignore',  \n",
+    "                           suppress_warnings=True, \n",
+    "                           stepwise=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1770.2022483405103"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stepwise_model.aic()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Energy Production</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1985-01-01</th>\n",
+       "      <td>72.5052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-02-01</th>\n",
+       "      <td>70.6720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-03-01</th>\n",
+       "      <td>62.4502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-04-01</th>\n",
+       "      <td>57.4714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985-05-01</th>\n",
+       "      <td>55.3151</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Energy Production\n",
+       "DATE                         \n",
+       "1985-01-01            72.5052\n",
+       "1985-02-01            70.6720\n",
+       "1985-03-01            62.4502\n",
+       "1985-04-01            57.4714\n",
+       "1985-05-01            55.3151"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "DatetimeIndex: 397 entries, 1985-01-01 to 2018-01-01\n",
+      "Data columns (total 1 columns):\n",
+      "Energy Production    397 non-null float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 6.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll train on 20 years of data, from the years 1985-2015 and test our forecast on the years after that and compare it to the real data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = data.loc['1985-01-01':'2016-12-01']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Energy Production</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-08-01</th>\n",
+       "      <td>115.5159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-01</th>\n",
+       "      <td>102.7637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-01</th>\n",
+       "      <td>91.4867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-01</th>\n",
+       "      <td>92.8900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-01</th>\n",
+       "      <td>112.7694</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Energy Production\n",
+       "DATE                         \n",
+       "2016-08-01           115.5159\n",
+       "2016-09-01           102.7637\n",
+       "2016-10-01            91.4867\n",
+       "2016-11-01            92.8900\n",
+       "2016-12-01           112.7694"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = data.loc['2015-01-01':]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Energy Production</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-01</th>\n",
+       "      <td>120.2696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-01</th>\n",
+       "      <td>116.3788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-01</th>\n",
+       "      <td>104.4706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-01</th>\n",
+       "      <td>89.7461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-01</th>\n",
+       "      <td>91.0930</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Energy Production\n",
+       "DATE                         \n",
+       "2015-01-01           120.2696\n",
+       "2015-02-01           116.3788\n",
+       "2015-03-01           104.4706\n",
+       "2015-04-01            89.7461\n",
+       "2015-05-01            91.0930"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Energy Production</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-09-01</th>\n",
+       "      <td>98.6154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-01</th>\n",
+       "      <td>93.6137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-01</th>\n",
+       "      <td>97.3359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-01</th>\n",
+       "      <td>114.7212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01</th>\n",
+       "      <td>129.4048</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Energy Production\n",
+       "DATE                         \n",
+       "2017-09-01            98.6154\n",
+       "2017-10-01            93.6137\n",
+       "2017-11-01            97.3359\n",
+       "2017-12-01           114.7212\n",
+       "2018-01-01           129.4048"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ARIMA(callback=None, disp=0, maxiter=None, method=None, order=(1, 1, 1),\n",
+       "      out_of_sample_size=0, scoring='mse', scoring_args={},\n",
+       "      seasonal_order=(2, 1, 2, 12), solver='lbfgs', start_params=None,\n",
+       "      suppress_warnings=True, transparams=True, trend=None,\n",
+       "      with_intercept=True)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stepwise_model.fit(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_forecast = stepwise_model.predict(n_periods=37)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([120.96887958, 109.86894842, 100.40214074,  90.58579401,\n",
+       "        92.18309427, 103.2733995 , 112.9492908 , 112.21561495,\n",
+       "       100.96895075,  91.9446821 ,  95.77292019, 111.23777429,\n",
+       "       120.36178749, 110.93821568, 102.00543891,  90.51827933,\n",
+       "        92.19307772, 102.8433255 , 112.08903959, 110.97099116,\n",
+       "       100.77485186,  92.04009423,  96.27309179, 109.57629618,\n",
+       "       119.90316977, 110.99147011, 101.5766366 ,  90.26345814,\n",
+       "        91.85280789, 102.86519884, 112.12271641, 111.28124902,\n",
+       "       101.12252718,  91.90083966,  95.65177025, 109.2012211 ,\n",
+       "       119.79168347])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "future_forecast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_forecast = pd.DataFrame(future_forecast,index = test.index,columns=['Prediction'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-01</th>\n",
+       "      <td>120.968880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-01</th>\n",
+       "      <td>109.868948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-01</th>\n",
+       "      <td>100.402141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-01</th>\n",
+       "      <td>90.585794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-01</th>\n",
+       "      <td>92.183094</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Prediction\n",
+       "DATE                  \n",
+       "2015-01-01  120.968880\n",
+       "2015-02-01  109.868948\n",
+       "2015-03-01  100.402141\n",
+       "2015-04-01   90.585794\n",
+       "2015-05-01   92.183094"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "future_forecast.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Energy Production</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DATE</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-01</th>\n",
+       "      <td>120.2696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-01</th>\n",
+       "      <td>116.3788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-01</th>\n",
+       "      <td>104.4706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-01</th>\n",
+       "      <td>89.7461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-01</th>\n",
+       "      <td>91.0930</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Energy Production\n",
+       "DATE                         \n",
+       "2015-01-01           120.2696\n",
+       "2015-02-01           116.3788\n",
+       "2015-03-01           104.4706\n",
+       "2015-04-01            89.7461\n",
+       "2015-05-01            91.0930"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x229cc136cf8>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.concat([test,future_forecast],axis=1).plot(figsize=(15,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_forecast2 = future_forecast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x229b9068400>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.concat([data,future_forecast2],axis=1).plot(figsize=(15,4))"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}