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Time Series ARIMA Chaotic
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Time Series ARIMA Chaotic
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import pandas as pd
import matplotlib.pyplot as plt
import numpy
import numpy as np
import pandas
import math
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from scipy.interpolate import spline
from pandas.tools.plotting import autocorrelation_plot
from statsmodels.tsa.arima_model import ARIMA
from scipy.stats import gaussian_kde
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
def norm(x):
return (x-np.min(x))/(np.max(x)-np.min(x))
dataframe = pd.read_csv('Chaotic_TimeSeries_turkey_elec.csv')
dataframe.head()
plt.plot(dataframe)
autocorrelation_plot(dataframe.ix[:,0])
### AVALIAR V3 LINHAS
model00 = ARIMA(np.array(dataframe.ix[:,0]), dates=None,order=(2,1,0))
model11 = model00.fit(disp=1)
model11.summary()
model11.forecast()
resid9=model11.resid
np.mean(abs(resid9))/max(np.array(dataframe.ix[:,0]))
x3 = resid9
x3 = x3[numpy.logical_not(numpy.isnan(x3))]
dftest13 = adfuller(x3, autolag='AIC')
dfoutput1 = pd.Series(dftest13[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
print('Dickey Fuller Test:\n',dfoutput1)
look_back=200
start=0
end=len(resid9)
lag=look_back
xx=np.array(resid9[start+lag:end])
yy=np.array(resid9[start:end-lag])
autocorrelation=np.corrcoef(xx,yy)
print('Autocorrelation of Residuals=',round(autocorrelation[0][1],3))
plt.plot(resid9)
plt.title('Residuals ARIMA')
plt.show()
print(pd.DataFrame(resid9).describe())
plt.hist(resid9)
density = gaussian_kde(resid9)
xs = np.linspace(-50,50,len(resid9))
density.covariance_factor = lambda : .25
density._compute_covariance()
plt.plot(xs,density(xs))
plt.show()
### DELETE OUTLIERS
thre=1.3
delete=np.where(resid9<np.mean(resid9)-thre*np.std(resid9))[0]
train0=np.delete(np.array(dataframe.ix[:,0]),delete)
train=np.sqrt(train0)
plt.hist(train)
rollmean = pd.rolling_mean(train, window=20)
rollstd = pd.rolling_std(train, window=20)
ts_log0 = np.log(train)
ts_log=pd.DataFrame(ts_log0).dropna()
decomposition = seasonal_decompose(np.array(ts_log).reshape(len(ts_log),),freq=100)
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid
z=np.where(seasonal==min(seasonal))[0]
period=z[2]-z[1]
look_back = period
plt.figure(figsize=(8,8))
plt.subplot(411)
plt.plot(ts_log, label='Original')
plt.legend(loc='upper left')
plt.subplot(412)
plt.plot(trend, label='Trend',color='red')
plt.legend(loc='upper left')
plt.subplot(413)
plt.plot(seasonal,label='Seasonality',color='green')
plt.legend(loc='upper left')
plt.subplot(414)
plt.plot(residual, label='Residuals',color='black')
plt.legend(loc='upper left')
plt.tight_layout()
from statsmodels.tsa.stattools import adfuller
dftest = adfuller(train, autolag='AIC')
dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
dfoutput
'''Not Stationary'''
x = train*seasonal
x = x[numpy.logical_not(numpy.isnan(x))]
dftest1 = adfuller(x, autolag='AIC')
dfoutput1 = pd.Series(dftest1[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
print('Dickey Fuller Test:\n',dfoutput1)
train=np.sqrt(train0)
modelP2= ARIMA(np.array(train), order=(2,1,0))
model_fit2 = modelP2.fit(disp=-1,tol=1e-28,maxiter=20000)
pred71 = model_fit2.forecast()[0]
model_fit2.summary()
print('Precision=',float((pred71[-1]**2)/train0[-1]))
print('Error=',100*(1-float((pred71[-1]**2)/train0[-1])),'percent')
print('Real Stock Value',train0[-1])
print('Predicted Stock Value',pred71[-1]**2)
############# PLOT
len(train0)
window=20
train=np.sqrt(train0)[0:2882]
for i in range(0,5):
modelP2= ARIMA(np.array(train)[-window:], order=(2,1,0))
model_fit2 = modelP2.fit(disp=0,tol=1e-20,transparams=True,trend='c')
pred71 = model_fit2.forecast()[0]
new=np.concatenate((train,pred71),axis=0)
train=new
predicted=train**2
predicted_ok=predicted[-6:]
dataframe3 = pd.read_csv('Chaotic_TimeSeries_turkey_elec.csv')
real_data=np.array(dataframe3.ix[:,0][3279:3285])
plt.figure(figsize=(8,5))
line1,=plt.plot(predicted_ok,marker='o',linewidth=2,color='red',label='PREDICTION')
line2,=plt.plot(real_data,marker='o',linewidth=2,color='blue',label='REAL ASSET VALUE')
plt.annotate('TODAY',(0,136.8))
for i in range(1,6):
plt.annotate('Error: {0}%'.format(round(100*(1-(real_data/predicted_ok))[i],2)),(i-.35,21000))
for k in range(0,5):
plt.annotate('Day + {}'.format(range(0,9)[k]+1),(k+.75,21500+k))
plt.title('ARIMA CHAOTIC TIME SERIES PREDICTION',fontsize=20)
plt.ylabel('Asset Value',fontsize=20)
plt.xlabel('Future Predictions (error)',fontsize=20)
plt.ylim(20000,25000)
plt.legend([line1,line2],loc='lower left')
plt.show()
print('REAL DATA:',real_data,'\n')
print('PREDICTED:',predicted_ok)