Can i forecast with discontinued data using ARIMA The Next CEO of Stack Overflow2019 Community Moderator ElectionAnomaly Detection In Univariate Time Series Data Using ARIMA In Python With UpdatingTime series forecast using SVM?Error when using seasonal arima in pythonHow do I use rnn to forecast to n periods with limited data?Forecast vs Prediction: What is the difference?Analysing spikes in demand to forecast future demanddemand forecast for B2BCan ARIMA be applied on a dataset of few months?forecast product demand in one week using machine learning approachContacts/Issues forecast based on orders
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Can i forecast with discontinued data using ARIMA
The Next CEO of Stack Overflow2019 Community Moderator ElectionAnomaly Detection In Univariate Time Series Data Using ARIMA In Python With UpdatingTime series forecast using SVM?Error when using seasonal arima in pythonHow do I use rnn to forecast to n periods with limited data?Forecast vs Prediction: What is the difference?Analysing spikes in demand to forecast future demanddemand forecast for B2BCan ARIMA be applied on a dataset of few months?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 or data 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
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
$endgroup$
add a comment |
$begingroup$
I have data for sales on monthly basis but few months information is not in csv file or data 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
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
$endgroup$
add a comment |
$begingroup$
I have data for sales on monthly basis but few months information is not in csv file or data 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
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
$endgroup$
I have data for sales on monthly basis but few months information is not in csv file or data 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
bipul kumarbipul kumar
317
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
edited 39 mins ago
bipul kumar
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
asked Mar 22 at 5:05
bipul kumarbipul kumar
317
317
add a comment |
add a comment |
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