- Table B.6 supplied to you contains global mean surface air temperature anomaly. Develop an appropriate ARIMA model and a method for forecasting
- Table B7. contains Whole Foods Market Stock Price daily closings Use the first 85% of the data and build an appropriate A RIMA model Come up with a forecasting method . Check your one-day two-day and three-day forecasts on the other 15% is of the dataset
- inc.dat contains gas pressure data from a utility company. Construct an appropriate ARIMA model. Report 95% prediction intervals and forecasts for a few time units
- Consider the ARIMA (0,1,1) model: (1 – B)Y_{t} = (1 – ƟB)Ɛ_{t}
Show that the one-step forecast is:
Ŷ_{t+1 }= Y_{t}– ƟƐ_{t}
Ŷ_{t+h}= Ŷ_{t+h-1} , h ≥ 2
- Let {Y_{t}} be a covariance stationary process with h-log correlation ƍ_{h}and variance σ^{2}. Derive an expression for the variance of the sample mean
= n^{-1 }_{i}
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