For this particular assignment, the data of different types of wine sales in the 20th century is to be analysed. Both of these data are from the same company but of different wines. As an analyst in...

For this particular
assignment, the data of different types of wine sales in the 20th century is to
be analysed. Both of these data are from the same company but of different
wines. As an analyst in the ABC Estate Wines, you are tasked to analyse and
forecast Wine Sales in the 20th century.

Data set for the
Problem:


Sparkling.csv


and


Rose.csv

Please do perform the
following questions on each of these two data sets separately.

1. 
   Read
   
   the data as an appropriate Time Series data and plot the data.
   




2. 
   Perform
   
   appropriate Exploratory Data Analysis to understand the data and also
   
   perform decomposition.
   




3. 
   Split
   
   the data into training and test. The test data should start in 1991.
   




4. 
   Build
   
   all the exponential smoothing models on the training data and evaluate the
   
   model using RMSE on the test data. Other additional models such as
   
   regression, naïve forecast models, simple average models, moving average
   
   models should also be built on the training data and check the performance
   
   on the test data using RMSE.
   




5. 
   Check
   
   for the stationarity of the data on which the model is being built on
   
   using appropriate statistical tests and also mention the hypothesis for
   
   the statistical test. If the data is found to be non-stationary, take
   
   appropriate steps to make it stationary. Check the new data for
   
   stationarity and comment.
   Note: Stationarity should be checked at alpha = 0.05.
   




6. 
   Build
   
   an automated version of the ARIMA/SARIMA model in which the parameters are
   
   selected using the lowest Akaike Information Criteria (AIC) on the
   
   training data and evaluate this model on the test data using RMSE.
   




7. 
   Build
   
   ARIMA/SARIMA models based on the cut-off points of ACF and PACFon
   
   the training data and evaluate this model on the test data using RMSE.
   




8. 
   Build
   
   a table with all the models built along with their corresponding
   
   parameters and the respective RMSE values on the test data.
   




9. 
   Based
   
   on the model-building exercise, build the most optimum model(s) on the
   
   complete data and predict 12 months into the future with appropriate
   
   confidence intervals/bands.
   




10. 
    Comment
    
    on the model thus built and report your findings and suggest the measures
    
    that the company should be taking for future sales.