what are the features of time
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In the original paper by Collopy and Armstrong (1992), eight features were identified by analytical procedures coded in the expert system while the rest relied on the forecasteras judgment and knowledge of the domain. The judgmental identification of these features, however, was time consuming and inconsistent, often taking over 5 minutes per series. In recent research, we have automated the feature identification process using simple statistical approaches. Consequently, time commitment to feature identification has dropped to under one minute per series without consequential decline in forecast accuracy.
Through extensive sensitivity analyses, we have found that causal forces are one of the key features of RBF. This feature represents the cumulative directional effect of the factors that influence the trends in a time series. As illustrated in the table above, causal forces can be classified in four ways based on their relation to historical trend. Several papers illustrate the use of causal forces in combining time series forecasts.
Through extensive sensitivity analyses, we have found that causal forces are one of the key features of RBF. This feature represents the cumulative directional effect of the factors that influence the trends in a time series. As illustrated in the table above, causal forces can be classified in four ways based on their relation to historical trend. Several papers illustrate the use of causal forces in combining time series forecasts.
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