The autoregressive integrated moving average (ARIMA) is one of the common forecasting methods used in time series analysis. It can be applied to any time series data, such as climate change, stock market performance, corporate resource utilization (e.g., computing servers, electricity), road traffic, etc.

You will use the ARIMA model to detect patterns in climate data and forecast near future climate change. Examine the "Time Series Dataset (Climate Change Prediction),"

https://www.kaggle.com/datasets/pranjalt/aurora-roundhacks

As a preliminary step, formulate a forecasting task that can be performed using this data and define the timeframe in which this forecast can be realistically made. Justify the choice of the forecasting task, as well as the forecasting window (days, weeks, months, etc.). Then, implement the ARIMA model in R, including the following steps (add more steps as you deem necessary):

1. Data preparation
   1. Define the prediction objective.
   2. Identify the data source.
   3. Load the appropriate libraries (e.g., forecast, tsutils).
   4. Preprocess the data.
   5. Explore and clean the data.
   6. Transform the raw data into a time series (using the ts() function).
   7. Plot and visually inspect the data.
   8. Assess the presence of seasonality using the tbats()
   9. Assess the presence of trends using the central moving average (CMA) using the cmav()function, and inspect the structure of the resulting time series.
   10. Use the decompose() and ggplot2() to perform and visualize the decomposition of the time series into data, seasonal, trend, and error(remainder).
   11. Use the seasplot() function to confirm the existence of a trend and absence of seasonality.
   12. Describe the data characteristics and any interesting phenomena observed.
2. Build the predictive model.
   1. Perform an exponential smoothing fit using the Holt-Winters algorithm.
   2. Interpret the results of Holt-Winters by inspecting the plot and explaining the alpha parameter.
   3. Run the ARIMA model using the arima()
   4. Test the coefficients using the lmtest library and coeftest() function, and interpret the results.
   5. Make a forecast using the Arima() function.
   6. Plot the results and interpret the visual output.
3. Analyze the findings.
   1. Explain and analyze the findings of the model and to what extent you were able to accomplish the task defined in step 1.a above.
   2. Summarize the benefit of performing exponential smoothing in the predictive process.
   3. Address the confidence interval used and its impact on predictions.
   4. Address and perform (as needed) additional steps such as detection of cyclical patterns, tweaking the parameters of the model, and comparing several prediction methods.
   5. Demonstrate ethical judgment to comply with legislation, regulations, and standards by articulating the knowledge of contemporary issues through a Christian worldview. Specifically, choose two key findings in your forecast and explain their relevancy to legislation, regulations, and standards. For example, you forecast an increase of two degrees, which will result into a certain impact on a particular industry. Specify the forecasted temperature and the expected industry impact. The legislative relevancy could be to mandate certain safety measures in an industrial plant. From a Christian worldview perspective, explain who you recommend should go about these mandates (e.g., the duty to the wellbeing of employees vs. the burden of compliance on the company). Then, explain and present your ideas on the regulatory aspect and standards.