ASSIGNMENT | What is your Prediction?

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)

Main Post: Once you have historical data, address the following:

1. State the variable you are forecasting.

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

3. Use the Time Series Forecasting Templates to forecast using moving average, weighted moving average, and exponential smoothing (see video in Unit 9 LiveBinder).

4. Copy/paste the results of each method into your post. Be sure to state the number of periods used in the moving average method, the weights used in the weighted moving average, and the value of α used in exponential smoothing.

5. Clearly state the “next period” prediction for each method.

SAMPLE SOLUTION

What is your Prediction?
Time series refers to observations which are sequentially collected in time. The observations are collected at equal time intervals which are usually noted by yt where (t=…. -1, 0, 1, 2….), where t indicates the time interval at which the data was collected.
A time series process is a stochastic process of collecting variable randomly which is indexed in time. Many statistical models assume that the observations are realizations of independent random variable which are important….