Can I forecast with discontinued data using ARIMA?
I have data for sales on monthly basis, but a few months' information is not in the CSV file or data file. Can I forecast or fill that missing month with other calculated values from present records?
Part of the 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('SARIMAX{}x{} - 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 SARIMAX{}x{}'.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)
Topic forecasting machine-learning-model python
Category Data Science