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Can i forecast with discontinued data using SARIMA
The Next CEO of Stack Overflow2019 Community Moderator ElectionTime series forecast using SVM?How do I use rnn to forecast to n periods with limited data?Forecast vs Prediction: What is the difference?How to use features when predicting aggregation?Analysing spikes in demand to forecast future demanddemand forecast for B2BWhat Machine Learning Algorithm could I use to determine some measure in a date?forecast product demand in one week using machine learning approachContacts/Issues forecast based on orders
$begingroup$
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
$endgroup$
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
$endgroup$
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
$endgroup$
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
python machine-learning-model forecasting
asked Mar 22 at 5:05
![](https://lh3.googleusercontent.com/-uc5dJM47CiE/AAAAAAAAAAI/AAAAAAAAA5M/heW_X37Cq9E/photo.jpg?sz=32)
![](https://lh3.googleusercontent.com/-uc5dJM47CiE/AAAAAAAAAAI/AAAAAAAAA5M/heW_X37Cq9E/photo.jpg?sz=32)
bipul kumarbipul kumar
316
316
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