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0

$begingroup$

I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.

part of code i am using:

AIC = []
SARIMAX_model = []
for param in pdq:
 for param_seasonal in seasonal_pdq:
 try:
 mod = sm.tsa.statespace.SARIMAX(train_data,
 order=param,
 seasonal_order=param_seasonal,
 enforce_stationarity=False,
 enforce_invertibility=False)

 results = mod.fit()

 print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
 AIC.append(results.aic)
 SARIMAX_model.append([param, param_seasonal])
 except:
 continue
print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

# Let's fit this model
mod = sm.tsa.statespace.SARIMAX(train_data,
 order=SARIMAX_model[AIC.index(min(AIC))][0],
 seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
 enforce_stationarity=False,
 enforce_invertibility=False)

python machine-learning-model forecasting

share|improve this question

asked 14 mins ago

bipul kumarbipul kumar

214

$endgroup$

add a comment | 

0

$begingroup$

I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.

part of code i am using:

AIC = []
SARIMAX_model = []
for param in pdq:
 for param_seasonal in seasonal_pdq:
 try:
 mod = sm.tsa.statespace.SARIMAX(train_data,
 order=param,
 seasonal_order=param_seasonal,
 enforce_stationarity=False,
 enforce_invertibility=False)

 results = mod.fit()

 print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
 AIC.append(results.aic)
 SARIMAX_model.append([param, param_seasonal])
 except:
 continue
print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

# Let's fit this model
mod = sm.tsa.statespace.SARIMAX(train_data,
 order=SARIMAX_model[AIC.index(min(AIC))][0],
 seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
 enforce_stationarity=False,
 enforce_invertibility=False)

python machine-learning-model forecasting

share|improve this question

asked 14 mins ago

bipul kumarbipul kumar

214

$endgroup$

add a comment | 

$begingroup$

I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.

part of code i am using:

AIC = []
SARIMAX_model = []
for param in pdq:
 for param_seasonal in seasonal_pdq:
 try:
 mod = sm.tsa.statespace.SARIMAX(train_data,
 order=param,
 seasonal_order=param_seasonal,
 enforce_stationarity=False,
 enforce_invertibility=False)

 results = mod.fit()

 print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
 AIC.append(results.aic)
 SARIMAX_model.append([param, param_seasonal])
 except:
 continue
print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

# Let's fit this model
mod = sm.tsa.statespace.SARIMAX(train_data,
 order=SARIMAX_model[AIC.index(min(AIC))][0],
 seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
 enforce_stationarity=False,
 enforce_invertibility=False)

python machine-learning-model forecasting

share|improve this question

asked 14 mins ago

bipul kumarbipul kumar

214

$endgroup$

AIC = []
SARIMAX_model = []
for param in pdq:
 for param_seasonal in seasonal_pdq:
 try:
 mod = sm.tsa.statespace.SARIMAX(train_data,
 order=param,
 seasonal_order=param_seasonal,
 enforce_stationarity=False,
 enforce_invertibility=False)

 results = mod.fit()

 print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
 AIC.append(results.aic)
 SARIMAX_model.append([param, param_seasonal])
 except:
 continue
print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

# Let's fit this model
mod = sm.tsa.statespace.SARIMAX(train_data,
 order=SARIMAX_model[AIC.index(min(AIC))][0],
 seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
 enforce_stationarity=False,
 enforce_invertibility=False)

share|improve this question

asked 14 mins ago

bipul kumarbipul kumar

214

share|improve this question

asked 14 mins ago

bipul kumarbipul kumar

214

asked 14 mins ago

bipul kumarbipul kumar

214

asked 14 mins ago

bipul kumarbipul kumar

214