Beispiel #1
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def generate_dataset_ARIMA(pun, first_day, meteo, varn):
    vector_date = np.array(pun[pun.columns[0]])
    vector_ore = np.array(pun[pun.columns[1]])
    target = np.array(pun[varn])
    global_dates = temp.dates(pd.Series(vector_date))
    vac_glob = temp.add_holidays(global_dates) 

    all_days = temp.generate_days(vector_ore, first_day)

    MD = replicate_meteo_variables(meteo, global_dates)

    aad = np.array([temp.convert_day_to_angle(v) for v in all_days]) 
    aore = np.sin(vector_ore*np.pi/24) 
    
    all_dict = {'holiday' : vac_glob, 'day' : aad, 'ora' : aore}
    all_dict.update(MD)
    
    FDF = pd.DataFrame(all_dict)
    
    return FDF, target
Beispiel #2
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                   sep="\t")

###################################################################
###### find missing dates #########################################
###################################################################

vector_dates = np.concatenate([
    np.array(data[data.columns[0]]),
    np.array(data2[data2.columns[0]]),
    np.array(data3[data3.columns[0]]),
    np.array(data4[data4.columns[0]]),
    np.array(data5[data5.columns[0]]),
    np.array(data6[data6.columns[0]])
])

global_dates = temp.dates(pd.Series(vector_dates))

missing = Functions_for_TSP.finding_missing_dates(global_dates, milano)
missing2 = Functions_for_TSP.finding_missing_dates(global_dates, torino)

test = Functions_for_TSP.update_meteo(milano, torino)
test.to_csv('storico_milano_aggiornato.txt', sep="\t", index=False)
missing2 = Functions_for_TSP.finding_missing_dates(global_dates, test)

missingca = Functions_for_TSP.finding_missing_dates(global_dates, ca)
missingpa = Functions_for_TSP.finding_missing_dates(global_dates, pa)
missingrc = Functions_for_TSP.finding_missing_dates(global_dates, rc)
missingfi = Functions_for_TSP.finding_missing_dates(global_dates, fi)

diffmiro = Functions_for_TSP.simulate_meteo(milano, roma)
difffiro = Functions_for_TSP.simulate_meteo(fi, roma)
Beispiel #3
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import statsmodels.api as sm
#from statsmodels.tsa import stattools
#from statsmodels.graphics import tsaplots
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import Functions_for_TSP
import statsmodels.tsa.arima_model

data = pd.read_excel("C:/Users/d_floriello/Documents/PUN/Anno 2010.xlsx")

import temp

pun = data["PUN"]
pun = data["CSUD"]
dates = temp.dates(data[data.columns[0]])
prova = pd.to_datetime(dates)
df = pd.DataFrame(pun)
df = df.set_index(prova)

dec = sm.tsa.seasonal_decompose(df, freq=24)
dec.plot()

min_season = np.array(dec.seasonal.ix[0:24])
plt.plot(min_season)

D, Y = temp.create_dataset(data, "ven")
Y = np.array(Y)

acf, Q, P, = statsmodels.tsa.stattools.acf(Y, nlags=48, qstat=True)
statsmodels.graphics.tsaplots.plot_acf(Y, lags=1000)
Beispiel #4
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ca = pd.read_table("C:/Users/d_floriello/Documents/PUN/storico_cagliari.txt", sep="\t")
pa = pd.read_table("C:/Users/d_floriello/Documents/PUN/storico_palermo.txt", sep="\t")
rc = pd.read_table("C:/Users/d_floriello/Documents/PUN/storico_reggiocalabria.txt", sep="\t")
fi = pd.read_table("C:/Users/d_floriello/Documents/PUN/storico_firenze.txt", sep="\t")


###################################################################
###### find missing dates #########################################
###################################################################

vector_dates = np.concatenate([np.array(data[data.columns[0]]), np.array(data2[data2.columns[0]]), 
                               np.array(data3[data3.columns[0]]),
                         np.array(data4[data4.columns[0]]), np.array(data5[data5.columns[0]]), 
                            np.array(data6[data6.columns[0]])])
                            
global_dates = temp.dates(pd.Series(vector_dates))

missing = Functions_for_TSP.finding_missing_dates(global_dates, milano)
missing2 = Functions_for_TSP.finding_missing_dates(global_dates, torino)

test = Functions_for_TSP.update_meteo(milano, torino)
test.to_csv('storico_milano_aggiornato.txt', sep="\t", index = False)
missing2 = Functions_for_TSP.finding_missing_dates(global_dates, test)

missingca = Functions_for_TSP.finding_missing_dates(global_dates, ca)
missingpa = Functions_for_TSP.finding_missing_dates(global_dates, pa)
missingrc = Functions_for_TSP.finding_missing_dates(global_dates, rc)
missingfi = Functions_for_TSP.finding_missing_dates(global_dates, fi)

diffmiro = Functions_for_TSP.simulate_meteo(milano,roma)
difffiro = Functions_for_TSP.simulate_meteo(fi,roma)
Beispiel #5
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import statsmodels.api as sm
#from statsmodels.tsa import stattools
#from statsmodels.graphics import tsaplots
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import Functions_for_TSP
import statsmodels.tsa.arima_model  

data = pd.read_excel("C:/Users/d_floriello/Documents/PUN/Anno 2010.xlsx")

import temp

pun = data["PUN"]
pun = data["CSUD"]
dates = temp.dates(data[data.columns[0]])
prova = pd.to_datetime(dates)
df = pd.DataFrame(pun)
df = df.set_index(prova)

dec = sm.tsa.seasonal_decompose(df, freq=24)
dec.plot()

min_season = np.array(dec.seasonal.ix[0:24])
plt.plot(min_season)

D,Y = temp.create_dataset(data, "ven")
Y = np.array(Y)

acf, Q, P, = statsmodels.tsa.stattools.acf(Y, nlags = 48, qstat = True)
statsmodels.graphics.tsaplots.plot_acf(Y, lags=1000)