Dear Participant, Please find below the Time Series Forecasting Project instructions:  • You have to submit 2 files: 1

Dear Participant, Please find below the Time Series Forecasting Project instructions: 
• You have to submit 2 files: 1. Answer Report: In this, you need to submit all the answers to all the questions in a sequential manner. It should include the detailed explanation of the approach used, insights, inferences, all outputs of codes like graphs, tables etc. Your report should not be filled with codes. You will be evaluated based on the business report. 2. Jupyter Notebook file: This is a must and will be used for reference while evaluating • Any assignment found copied/ plagiarized with another person will not be graded and marked as zero. • Please ensure timely submission as a post-deadline assignment will not be accepted. 
Problem 1 for the Data Set Shoe-sales.csv: You are an analyst in the IJK shoe company and you are expected to forecast the sales of the pairs of shoes for the upcoming 12 months from where the data ends. The data for the pair of shoe sales has been given to you from January,1980 to July,1995. 
Problem 2 for the Data Set SoftDrink.csv: You are an analyst in the RST soft drink company and you are expected to forecast the sales of the production of the soft drink for the upcoming 12 months from where the data ends. The data for the production of soft drink has been given to you from January,1980 to July,1995. 

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

RUBRIC

Questions Points 1. Read the data as an appropriate Time Series data and plot the data. 2 2. Perform appropriate Exploratory Data Analysis to understand the data and also perform decomposition. 6 3. Split the data into training and test. The test data should start in 1991. 2 4. Build various exponential smoothing models on the training data and evaluate the model using RMSE on the test data. Other models such as regression, naive forecast models, simple average models etc. should also be built on the training data and check the performance on the test data using RMSE. 18 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. 3 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. 10 7. Build ARIMA/SARIMA models based on the cut-off points of ACF and PACF on the training data and evaluate this model on the test data using RMSE. g 8. Build a table with all the models built along with their corresponding parameters and the respective RMSE values on the test data. 2 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. 3 10. Comment on the model thus built and report your findings and suggest the measures that the company should be taking for future sales. 5 Total 60 

Note:

The points for the two data sets are divided equally.

All the very best!

Regards,

Program Office