a. Plot the series and explain what can be learned from this plot. b. Calculate the first 24 autocorrelations for the series, and explain what characteristics of the data are shown in the ACF and PACF. c. Suggest and estimate an optimal ARIMA model. d. Estimate the ARIMA model that you believe to be a good candidate for forecasting electricity usage. Test the Ljung-Box statistic and report your findings. Finally, plot the first 24 autocorrelations of the residuals to your best model. 4. Phot the series and explain whar can be learsed foom this phee. of the data ane shinus in the ACF and PACF 6. Suggest and evitmate an opitimal ARIMA mosel. the fint 24 autiococrelations of the revituals to yoar bes tricel. a. Plot the series und explain what can be learned from thit plok b. Calculate the fint 24 autoconrelations for the series, and exphais a fat characterintis: of the data are shown in the ACF and PNCF. 5. Suggest and estimate an optimal ARIMA tooklel. d. Fstimate the ARIMA model that you believe to be a good candidate for forecating electricity usage, Test the Ljung-fios statistic and report your fincings. Finally. plote the fint 24 autacomelations of the residuals to your bes model. a. Plot the series and explain what can be learned from this plot. b. Calculate the first 24 autocorrelations for the series, and explain what characteristics of the data are shown in the ACF and PACF. c. Suggest and estimate an optimal ARIMA model. d. Estimate the ARIMA model that you believe to be a good candidate for forecasting electricity usage. Test the Ljung-Box statistic and report your findings. Finally, plot the first 24 autocorrelations of the residuals to your best model. a. Plot the series and explain what can be learned from this plot. b. Calculate the first 24 autocorrelations for the series, and explain what characteristics of the data are shown in the ACF and PACF. c. Suggest and estimate an optimal ARIMA model. d. Estimate the ARIMA model that you believe to be a good candidate for forecasting electricity usage. Test the Ljung-Box statistic and report your findings. Finally, plot the first 24 autocorrelations of the residuals to your best model. 4. Phot the series and explain whar can be learsed foom this phee. of the data ane shinus in the ACF and PACF 6. Suggest and evitmate an opitimal ARIMA mosel. the fint 24 autiococrelations of the revituals to yoar bes tricel. a. Plot the series und explain what can be learned from thit plok b. Calculate the fint 24 autoconrelations for the series, and exphais a fat characterintis: of the data are shown in the ACF and PNCF. 5. Suggest and estimate an optimal ARIMA tooklel. d. Fstimate the ARIMA model that you believe to be a good candidate for forecating electricity usage, Test the Ljung-fios statistic and report your fincings. Finally. plote the fint 24 autacomelations of the residuals to your bes model. a. Plot the series and explain what can be learned from this plot. b. Calculate the first 24 autocorrelations for the series, and explain what characteristics of the data are shown in the ACF and PACF. c. Suggest and estimate an optimal ARIMA model. d. Estimate the ARIMA model that you believe to be a good candidate for forecasting electricity usage. Test the Ljung-Box statistic and report your findings. Finally, plot the first 24 autocorrelations of the residuals to your best model