A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to reference the website). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time.

• Currency price: XE (http://www.xe.com/currencyconverter/)

• GNP: Trading Economics (http://www.tradingeconomics.com/united-states/gross-national-product)

• Average home sales: National Association of Realtors (http://www.realtor.org/topics/existing-home-sales)

• College tuition: National Center for Education Statistics (https://nces.ed.gov/fastfacts/display.asp?id=76)

• Weather temperature or precipitation: (http://www.weather.gov/help-past-weather)

• Stock price: Yahoo Finance (https://finance.yahoo.com)

Once you have historical data, address the following:

1. Apply quantitative forecasting methods in time-series modeling.

a. State the variable you are forecasting.

b. Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values.

c. Compute moving average and weighted moving average in a time-series model.

i. Use the Excel Workbooks for this module to forecast the next period’s value using moving average, and weighted moving average

ii. Copy/paste the results of each method into your word document.

iii. Be sure to state the number of periods used in the moving average method and the weights used in the weighted moving average. Clearly state the “next period” prediction for each method.

iv. Determine which of the two forecasts should be chosen and give the rationale for the decision.

d. Identify variables for a regression model.

i. Determine which variable from the time series forecast would be an appropriate dependent variable (X) and tell why.

ii. Determine which variable from the time series forecast would be an appropriate independent variable (Y) and tell why.

e. Develop a simple linear regression model.

i. Use the regression function found in Data Analysis located in Microsoft Excel to determine the linear regression model.

ii. Based upon the values given, what is the valid dependent variable range?