Can i forecast with discontinued data using SARIMA

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.

enter image description here

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('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)

asked 9 mins ago

bipul kumar

214

add a comment |

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.

enter image description here

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('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)

asked 9 mins ago

bipul kumar

214

add a comment |

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.

enter image description here

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('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)

asked 9 mins ago

bipul kumar

214

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.

enter image description here

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('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)

python machine-learning-model forecasting

asked 9 mins ago

bipul kumar

214

asked 9 mins ago

bipul kumar

214

asked 9 mins ago

bipul kumar

214

asked 9 mins ago

bipul kumar

214

asked 9 mins ago

bipul kumar

214

add a comment |

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