As part my of PSTAT 262 (Applied Bayesian Time Series) next semester, I plan on having students pick out a data set to make both non-model and model based forecasts for the series. My only requirement for the data are that the correct forecasts cannot already exist.  An additional suggestion is that the forecasts come true soon so that forecasts can be compared with realizations.

Examples of unacceptable datasets are

• Dow Jones Index from 1990-2000
• Dollars spent on US health care until 2005
• Abundance of passenger pigeons

Examples of acceptable datasets are

• Yearly average S&P 500 Index from 1949 to present
• Mean global temperature in the past millennium
• Median U.S. House price for the past 40 years

Examples of ideal datasets are

• Daily price of Coca-Cola stock since 2000
• Number of daily airline passengers for the past 5 years
• Weekly sunspot activity since 1977

Students will be asked to forecast their time series at two different time points: a relatively short forecast and one relatively long.