All these four sample runs use real-world data and you will see that, like with all complex phenomena, it takes some cunning to design very good models: some times good results can be obtained using raw data, but other times by smoothing the data you can obtain better results. In three of the sample runs (Dow Jones, Exchange Rate, and Earthquakes) an integral smoothing algorithm was used, and if you compare both the plots and behavior of these time series with the Sunspots series (in this case, the original raw data was used), you will see that, as a rule, it is much easier to create good models using smoothed data.

Notwithstanding, GeneXproTools has exceptional tools to assist you in creating good predictive models. The most important is the vast number of built-in functions that allow you to create really complex models. As an example, see the default functions we recommend for Time Series Prediction in the Run Templates. Try experimenting with different function sets by mixing some functions chosen by hand with others picked randomly with the Random button in the Functions Panel.

Also important is being able to make predictions quickly with all the best-of-generation models created in a run (they are all kept in the History Panel) and then choosing the best one. For that you just have to select the model you want to test in the History Panel and then make predictions with it in the Predictions Panel.

These are some of the tips and strategies you might want to try with the four sample runs GeneXproTools offers for Time Series Prediction:

The Dow Jones sample run is an interesting time series application that uses monthly closings of the Dow-Jones industrial index. For this sample run, each value from the original raw time series (data file dj.dat obtained from the Time Series Data Library) was replaced by its moving average using a smoothing period p = 10. You will see that GeneXproTools is able to make very good predictions about the monthly closings of the Dow-Jones if you choose Testing Mode and then compare past known behavior with your models' predictions in the Predictions Panel.

The Exchange Rate sample run uses the monthly average exchange rate of Australian dollar for US dollar. For this sample run, each value from the original raw time series (data file usexchm.dat obtained from the Time Series Data Library) was replaced by its moving average using a smoothing period p = 12. You will see that GeneXproTools is able to make very good predictions about exchange rates if you choose Testing Mode and then compare past known behavior with your models' predictions in the Predictions Panel.

In the Earthquakes sample run a time series with the number of earthquakes per year with magnitude 7.0 or greater in the period 1900-1998 is used. And again, each value from the original raw time series (data file earthq.dat obtained from the Time Series Data Library) was replaced by its moving average using a smoothing period p = 10. You will see that GeneXproTools is able to make very good predictions about earthquakes if you choose Testing Mode and then compare past known behavior with your models' predictions in the Predictions Panel.

The Sunspots sample run uses the well-known Wolfer sunspots data. For this sample run, 100 observations of the Wolfer sunspots series were used with an embedding dimension of 10 and a delay time of one. You will see that GeneXproTools is able to make very good predictions about sunspots if you choose Testing Mode and then compare past known behavior with your models' predictions in the Predictions Panel.