For this activity, in 500-750 words, answer the following:

1. Construct a time plot of the data and inspect the graph for any anomalies. This time plot should suggest whether any differencing is needed. Explain.
2. Use the autocorrelation and partial autocorrelation plots to identify and select the preliminary values of the auto regression (AR) order, p, the order of differencing, d, and the moving average order, q. Explain your findings.
3. Fit and train the ARIMA model based on your selected p, d, q values.
4. Evaluate your model statistically by interpreting its t-test, p values, R Squared, adjusted R squared, MAE, and MAPE. Interpret the results.
5. Forecast the next five periods and report the results.
6. Parameter Tuning: fit another ARIMA model with a different value(s) for the p, d, q parameters. Compare both models and interpret the findings. Which one is better in terms of white noise, variation, unusual patterns, trends, seasonality.., etc. Why?
7. Evaluate each model using the walk forward validation. Explain.