Time series (v) Stock Analysis __ Stock

First import the related module

Import pandas as PD
import pandas_datareader
import datetime
import Matplotlib.pylab as PLT
import Seaborn as SNS from
Matplotlib.pylab import style to
Statsmodels.tsa.arima_model import Arima
from Statsmodels.graphics.tsaplots Import PLOT_ACF, PLOT_PACF

Set style

Style.use (' Ggplot ')    
plt.rcparams[' font.sans-serif '] = [' Simhei '] 
plt.rcparams[' axes.unicode_minus '] = False  

Read into data

Stockfile = ' data/t10yr.csv ' stock
= Pd.read_csv (Stockfile, Index_col=0, parse_dates=[0])
Stock.head (10)

Resampling

Stock_week = stock[' Close '].resample (' W-mon '). Mean () stock_train
= stock_week[' 2000 ': ' 2015 ']

Drawing

Stock_train.plot (figsize= (12,8))
Plt.legend (bbox_to_anchor= (1.25, 0.5))
Plt.title ("Stock close")
Sns.despine ()
plt.show ()

Found that volatility is too large, so do a first-order difference

Stock_diff = Stock_train.diff ()
Stock_diff = Stock_diff.dropna () plt.figure () plt.plot (
Stock_diff)
Plt.title (' First order difference ')
plt.show ()

Next, we determine the P value and Q value of the model by ACF,PACF

Fig=plt.figure ()
Ax1=fig.add_subplot (211)
Ax2=fig.add_subplot (212)
ACF = PLOT_ACF (Stock_diff, lags= 20,ax=ax1,title= "ACF")
PACF = PLOT_PACF (Stock_diff, lags=20,ax=ax2,title= "PACF")
plt.show ()

You can see P,q 1 on it.

Training

Model = ARIMA (Stock_train, order= (1, 1, 1), freq= ' W-mon ') result
= Model.fit ()

Forecast

pred = result.predict (' 20140609 ', ' 20160701 ', dynamic=true, typ= ' levels ')
print (pred)

Forecast results
2014-06-09 2.463559
2014-06-16 2.455539
2014-06-23 2.449569
2014-06-30 2.444183
2014-07-07 2.438962
2014-07-14 2.433788
2014-07-21 2.428627
2014-07-28 2.423470
2014-08-04 2.418315
2014-08-11 2.413159
2014-08-18 2.408004
2014-08-25 2.402849
2014-09-01 2.397693
2014-09-08 2.392538
2014-09-15 2.387383
2014-09-22 2.382227
2014-09-29 2.377072
2014-10-06 2.371917
2014-10-13 2.366761
2014-10-20 2.361606
2014-10-27 2.356451
2014-11-03 2.351296
2014-11-10 2.346140
2014-11-17 2.340985
2014-11-24 2.335830
2014-12-01 2.330674
2014-12-08 2.325519
2014-12-15 2.320364
2014-12-22 2.315208
2014-12-29 2.310053
...
2015-12-07 2.057443
2015-12-14 2.052288
2015-12-21 2.047132
2015-12-28 2.041977
2016-01-04 2.036822
2016-01-11 2.031666
2016-01-18 2.026511
2016-01-25 2.021356
2016-02-01 2.016200
2016-02-08 2.011045
2016-02-15 2.005890
2016-02-22 2.000735
2016-02-29 1.995579
2016-03-07 1.990424
2016-03-14 1.985269
2016-03-21 1.980113
2016-03-28 1.974958
2016-04-04 1.969803
2016-04-11 1.964647
2016-04-18 1.959492
2016-04-25 1.954337
2016-05-02 1.949181
2016-05-09 1.944026
2016-05-16 1.938871
2016-05-23 1.933716
2016-05-30 1.928560
2016-06-06 1.923405
2016-06-13 1.918250
2016-06-20 1.913094
2016-06-27 1.907939
Freq:w-mon, length:108, Dtype:float64

Image Display Results

Plt.figure (figsize= (6, 6))
plt.xticks (rotation=45)
plt.plot (pred)
plt.plot (stock_train)
Plt.show ()