Forecasting and Indices Forecasting is a process in which statements or conclusions are made regarding the outcome of events that have not yet happened. Forecasting is predicting what the could look like. There are many examples of forecasting. Estimating or predicting can be referred to as formal statistical methods that employ time series, cross-sectional or longitudinal data. Forecasting can be use to estimate a wide variety of issues, weather related events, the use of resources, sales, profit or loss and even staffing needs for a particular project. Coupling these findings with good planning can give a company a good indication of what the future should look like. Forecasting can use many different methods. Evaluating your objectives and conditions relevant to the individual situations can help in choosing the correct method to use. Another option is to use several methods and compare or combine the results, as many times, there is no one “best” method. “The forecast error is the difference between the actual value and the forecast value for the corresponding period. where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t.
Measures of aggregate error:
Mean absolute error (MAE) Mean Absolute Percentage Error (MAPE)
Mean Absolute Deviation (MAD)
Percent Mean Absolute Deviation (PMAD)
Mean squared error (MSE) Root Mean squared error (RMSE)
Forecast skill (SS) Average of Errors (E)

(http://en.wikipedia.org/wiki/Forecasting) An index is a statistical measure of change. In the financial markets, each index is based upon change from a base value and has calculation methodology specific to the individual indices. Percentage of change is generally more important than the numerical change. Indices are used in a variety of settings. The most common is the stock market.