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ARIMA Modeling and Forecasting 2a. Split the data in Trainin…

ARIMA Modeling and Forecasting 2a. Split the data in Training and Testing sets. Leave the last 8 observations for testing, the rest will be your training set. You will apply the same trend-seasonality model from Question 1d on the Training set and forecast the corresponding data for the testing set. Calculate the MAPE and interpret it. How does it compare to the MAPE in 1(d)? Why is it different? Hints:  – Keep in mind that modeling factors may require extra steps on the data preparation.  – You can use predict, or predict.gam for your predictions.  2b. Fit an ARMA model using the residuals from the model in Question 2a. Find the order of the ARMA model using a max order 6 for p and q, and 1 for d.  Use AICc as the criterion for the order selection. What are the selected orders? Use the forecast from the model you find and add them to the forecast values in 2a. Comment on what would be the logic of this procedure? 2c. Fit an SARIMA model to the training dataset using ARIMA orders (10,1,8) and seasonal orders (1,0,1). Comment on the model goodness of fit. 2d. Use the model from Question 2c to forecast the next eight time points (testing dataset) using the **8 lags ahead approach**. Overlay the observed versus predicted values for both series, including 95% confidence intervals. Calculate the MAPE of the prediction and comment on the prediction performance of the model. What approach would you use for this data? Why?

The R Markdown and Jupyter Notebook files include the questi…

The R Markdown and Jupyter Notebook files include the questions, the empty code chunk sections for your code, and the text blocks for your responses. Answer the questions below by completing either the R Markdown or the Jupyter Notebook file.  You will submit an html file created using either template.  You may make slight adjustments to get the file to knit/convert but otherwise keep the formatting the same. Once you’ve finished answering the questions, submit your responses in a single knitted file (just like the homework data analysis assessments). There are two questions  within each with sub-questions. The number of points for each question is provided for each question. Partial credit may be given if your code is correct but your conclusion is incorrect or vice versa. Next Steps: Save the template of your choice in your R working directory – the same directory where you will download the data file for this midterm exam. Having both files in the same directory will help in reading the .csv file. Read the question and create the R code necessary within the code chunk section immediately below each question. Knitting this file will generate the output and insert it into the section below the code chunk. Type your answer to the questions in the text block provided immediately after the response prompt. Once you’ve finished answering all questions, knit this file and submit the knitted file as HTML on Canvas. Please note that there will be a penalty applied to your grade if you do not submit the html file (if you submit instead the unknitted file). Make sure to knit your work as you answer the questions one by one to avoid issues with knitting the file in the last minutes of the exam. Ready? Let’s begin. We wish you the best of luck! Data Set  (right-click the link and select to open in new window/tab) monthly-beer-production-in-austr.csv R Markdown Starter Template Midterm 1 Fall 2024 Template.Rmd Jupyter Notebook Starter Template Midterm 1 Template.ipynb