使用Python做数据分析学习前的准备

https://gitee.com/davylw/datasets/tree/master

http://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html

https://github.com/robjhyndman/forecast

pip install statsmodels
pip install pmdarima
pip install forecast

https://pkg.yangzhuoranyang.com/tsdl/

git clone https://gitee.com/davylw/datasets.git

https://sourceforge.net/projects/openofficeorg.mirror/

http://www.openoffice.org/download/

http://www.dayanzai.me/?s=%E5%8A%9E%E5%85%AC

from pmdarima.datasets import load_winein

load_wineind(True).head()

"""
=======================
Simple auto_arima model
=======================


This is a simple example of how we can fit an ARIMA model in several lines
without knowing anything about our data or optimal hyper parameters.

.. raw:: html

   <br/>
"""
from matplotlib import pyplot as plt
import numpy as np
from pmdarima import model_selection
import pmdarima as pm
print(__doc__)

# Author: Taylor Smith <taylor.smith@alkaline-ml.com>


# #############################################################################
# Load the data and split it into separate pieces
data = pm.datasets.load_wineind()
train, test = model_selection.train_test_split(data, train_size=150)

# Fit a simple auto_arima model
arima = pm.auto_arima(train, error_action='ignore', trace=True,
                      suppress_warnings=True, maxiter=10,
                      seasonal=True, m=12)

# #############################################################################
# Plot actual test vs. forecasts:
x = np.arange(test.shape[0])
plt.scatter(x, test, marker='x')
plt.plot(x, arima.predict(n_periods=test.shape[0]))
plt.title('Actual test samples vs. forecasts')
plt.show()