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EGC1

Forecasting

QuestionAnswer
know the various components of a time series The usual assumption is that four separate components — trend, cyclical, seasonal, and irregular — combine to provide specific values for the time series.
Time series A set of observations of a variable measured at successive points in time or over successive periods of time.
Forecast A projection or prediction of future values of a time series.
Time series method Forecasting method that is based on the use of historical data that are restricted to past values of the variable we are trying to forecast.
Causal forecasting methods Forecasting methods that are based on the assumption that the variable we are trying to forecast exhibits a cause–effect relationship with one or more other variables.
Trend The gradual shift or movement of the time series to relatively higher or lower values over a longer period of time.
Cyclical component The component of the time series that accounts for the periodic above-trend and below-trend behavior of the time series lasting more than one year.
Seasonal component The component of the time series that represents the variability in the data due to seasonal influences.
Irregular component The component of the time series that accounts for the random variability in the time series.
Moving averages A smoothing method that uses the average of the most recent n data values in the time series as the forecast for the next period.
Mean squared error (MSE) An approach to measuring the accuracy of a forecasting method. This measure is the average of the sum of the squared differences between the actual time series values and the forecasted values.
Weighted moving averages A smoothing method that uses a weighted average of the most recent n data values as the forecast.
Exponential smoothing A smoothing method that uses a weighted average of past time series values as the forecast; it is a special case of the weighted moving averages method in which we select only one weight—the weight for the most recent observation.
Smoothing constant In the exponential smoothing model, the smoothing constant is the weight given to the actual value of the time series in period t.
Mean absolute deviation (MAD) A measure of forecast accuracy. The average of the absolute values of the forecast errors.
Multiplicative time series model When the four components of trend, cyclical, seasonal,& irregular are present,we obtain Yt=Tt*Ct*St*It.When cyclical effects aren't modeled,we obtain Yt=Tt*St*It.
Seasonal index A measure of the seasonal effect on a time series. A seasonal index above 1 indicates a positive effect, a seasonal index of 1 indicates no seasonal effect, and a seasonal index less than 1 indicates a negative effect.
Deseasonalized time series A time series that has had the effect of season removed by dividing each original time series observation by the corresponding seasonal index.
Regression analysis A statistical technique used to develop a mathematical equation showing how variables are related.
Autoregressive model A regression model in which the independent variables are previous values of the time series.
Delphi method A qualitative forecasting method that obtains forecasts through group consensus.
Scenario writing A qualitative forecasting method that consists of developing a conceptual scenario of the future based on a well-defined set of assumptions.
Scenario writing A qualitative forecasting method that consists of developing a conceptual scenario of the future based on a well-defined set of assumptions.
Created by: mmoreno12 on 2012-06-25



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