upfi = Functions_for_TSP.generate_simulated_meteo_dataset(fi, roma)
#################################################################
variables = data.columns[[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20
]]
DF = pd.concat([
data[variables], data2[variables], data3[variables], data4[variables],
data5[variables]
],
axis=0)
data6 = data6[variables]
tdf, ty = Functions_for_TSP.generate_dataset_ARIMA(data6, "gio", roma, "CSUD")
tdfcov = pd.concat([tdf, pd.Series(ty)], axis=1)
np.linalg.det(tdfcov.corr().as_matrix())
df, y = Functions_for_TSP.generate_dataset_ARIMA(DF, "ven", roma, "CSUD")
dfcov = pd.concat([df, pd.Series(y)], axis=1)
np.linalg.det(dfcov.corr().as_matrix())
aicg = statsmodels.tsa.stattools.arma_order_select_ic(y,
ic=['aic', 'bic'],
max_ar=24,
max_ma=12)
tot_model = statsmodels.tsa.arima_model.ARIMA(endog=y,
roma_dec = sm.tsa.seasonal_decompose(rmedia, freq=365)
roma_dec.plot()
plt.plot(roma['Tmedia'])
upfi = Functions_for_TSP.generate_simulated_meteo_dataset(fi,roma)
#################################################################
variables = data.columns[[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20]]
DF = pd.concat([data[variables],data2[variables],data3[variables],data4[variables],
data5[variables]], axis=0)
data6 = data6[variables]
tdf,ty = Functions_for_TSP.generate_dataset_ARIMA(data6,"gio",roma, "CSUD")
tdfcov = pd.concat([tdf,pd.Series(ty)],axis=1)
np.linalg.det(tdfcov.corr().as_matrix())
df, y = Functions_for_TSP.generate_dataset_ARIMA(DF,"ven",roma, "CSUD")
dfcov = pd.concat([df,pd.Series(y)],axis=1)
np.linalg.det(dfcov.corr().as_matrix())
aicg = statsmodels.tsa.stattools.arma_order_select_ic(y, ic = ['aic','bic'],max_ar=24, max_ma=12)
tot_model = statsmodels.tsa.arima_model.ARIMA(endog=y, order=[24,1,12],exog = df.as_matrix()).fit(trend = 'c', maxiter = 100)
#for i in range(1,20,1):
# plt.plot(pd.ewma(pd.Series(y), span=8670))
