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EGC1
Forecasting
| Question | Answer |
|---|---|
| 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
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