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  • Forecast (5.4)

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Tools | Time Series

Updated Feature: Network Temporalization

If a dataset describes time series, Network Temporalization can directly create Temporal Clones of the selected variables.

Prior to the 5.4 release, the clones were created as lead variables, denoted by Node_t, Node_t+1, ..., Node_t+n.

Now, Network Temporalization creates clones as lagged variables, denoted by Node_t, Node_t-1, ..., Node_t-n. An example is shown below.

Example
 

Updated Feature: Forecasting | Prediction Mode

The Prediction Mode now offers two options for forecasting:

  • Predicting the Modal Value, i.e. the value of the state that has the highest posterior probability.
  • Predicting the Expected Value: the Expected Value is computed from the entire posterior probability distribution.

Updated Feature: Forecasting | Output

You now have the option of saving the forecasted values as an internal Test Database, i.e. as a test set, or to save them as a new file.