def main():
series = get_client_portion(get_client(1), "950105", 400)
segmented = segment(series)
time_serie_plotter(series, segmented)
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 16 14:27:36 2019
@author: ASUS
"""
from statsmodels.tsa.seasonal import seasonal_decompose
import statsmodels.tsa.stattools
import pylab
from client_charger import get_client, get_client_portion
import pandas as pd
serie = get_client_portion(get_client(4), "950101", 1000)
data = pd.DataFrame([serie])
data.reset_index(inplace=True)
data.index = pd.to_datetime(data.index)
detrended = list()
o = data.values.tolist()
flat_list = [item for sublist in o for item in sublist]
o = flat_list
for i in range(1, len(o)):
value = o[i] - o[i - 1]
print(o[i])
detrended.append(value)
result = seasonal_decompose(detrended, model='additive', freq=365)
ACF = statsmodels.tsa.stattools.acf(data)
print(len(ACF))
pylab.plot(data.values.ravel(), label='original serie')
MinRandomForest = 0
MoyRandomForest = []
RandomForestMSE = []
MaxArima = 0
MinArima = 0
MoyArima = []
ArimaMSE = []
print("start")
accounts = accounts_importer()
k = 0
for i in range(1, 1000):
if i in accounts:
print(k)
serie = get_client_portion(get_client(i), "940105", 800)
mx, mn, mse, moy = polynomial_regression_model.main(serie)
if mx > MaxPolReg:
MaxPolReg = mx
if mn > MinPolReg:
MinPolReg = mn
MoyPolReg.append(moy)
PolRegMSE.append(mse)
mx, mn, mse, moy = decision_tree_model.main(serie)
if mx > MaxDecTree:
MaxDecTree = mx
if mn > MinDecTree:
MinDecTree = mn
MoyTree.append(moy)
DecTreeMSE.append(mse)
# -*- coding: utf-8 -*- """ Created on Thu Apr 11 21:07:25 2019 @author: ASUS """ import svm_model import arima_model from pymongo import MongoClient import pylab from client_charger import get_client ,get_client_portion serie = get_client_portion(get_client(4),"950105",800) svm_prediction,rmse_svm=svm_model.main(serie) test,arima_prediction,rmse_arima=arima_model.main(serie) flat_list = [item for sublist in arima_prediction.tolist() for item in sublist] arima_prediction = flat_list svm_prediction = svm_prediction.tolist() """ pyplot.plot(test) pyplot.plot(svm_prediction, color='red') pyplot.show() """ pylab.plot( test, label='true value') pylab.plot( svm_prediction, label='svm_prediction') pylab.plot( arima_prediction, label='arima_prediction') pylab.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 3 13:11:49 2019
@author: ASUS
"""
from client_charger import get_client, get_client_portion
import pandas as pd
series = get_client_portion(get_client(1), "950105", 700)
X = series.values
Y = series.index.values
day = []
month = []
year = []
i = 0
for y in Y:
day.append(y.day)
month.append(y.month)
year.append(y.year)
data = {'day': day, 'month': month, 'year': year, 'balance': X}
df = pd.DataFrame(data)
print(data_history)
print(data_pred)
pyplot.plot(test)
pyplot.plot(predictions, color='red')
pyplot.show()
print(data_history[10])
print(data_pred[10])
print(test[10])
print(predictions[10])
"""
for i in range(0,10):
if i in accounts:
"""
main(get_client_portion(get_client(1), "960105", 80))
"""
predictions = np.array(predictions)
Max = 0
Min = 0
for i in range(len(test)):
localDiff = test[i]-predictions[i]
if localDiff < 0 and (abs(localDiff)/test[i]) > Max:
Max=(abs(localDiff)/test[i])
elif localDiff > 0 and (localDiff/test[i]) > Min:
Min = (localDiff/test[i])
return Max,Min
"""