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#!/usr/bin/env python
# coding: utf-8
# DO NOT EDIT
# Autogenerated from the notebook tsa_dates.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT
# # Dates in timeseries models
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.tsa.ar_model import AutoReg, ar_select_order
plt.rc("figure", figsize=(16, 8))
plt.rc("font", size=14)
# ## Getting started
data = sm.datasets.sunspots.load()
# Right now an annual date series must be datetimes at the end of the
# year.
from datetime import datetime
dates = pd.date_range("1700-1-1", periods=len(data.endog), freq="YE-DEC")
# ## Using Pandas
#
# Make a pandas TimeSeries or DataFrame
data.endog.index = dates
endog = data.endog
endog
# Instantiate the model
selection_res = ar_select_order(endog,
9,
old_names=False,
seasonal=True,
period=11)
pandas_ar_res = selection_res.model.fit()
# Out-of-sample prediction
pred = pandas_ar_res.predict(start="2005", end="2027")
print(pred)
fig = pandas_ar_res.plot_predict(start="2005", end="2027")
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