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Fig. 9 Same as Fig. 8 for forecasting event B8 Fig. 10 Same as Fig. 8 for forecasting event B9 Fig. 11 Same as Fig. 8 for forecasting event B10 Fig. 12 Same as Fig. 8 for forecasting event B11.

Seven sequences are selected from group 2 to forecast event B9, whereas 17 are selected to forecast event B11.

Overall, this procedure opens new opportunities for operational forecasting by substantially increasing the forecast event rates.

Fig. 8 Modeled sequences selected from group 1 and group 2 for forecasting event B7.

Figures 9, 10, 11 and 12 show the selected sequences used to forecast event B8 B11.

We show that announcements of upcoming events tend to localize within a small geographic region, allowing us to forecast event locations which are not explicitly mentioned in text.

The forecast errors decreased as the number of the ensemble sequences [J in Eq. (7)] increased, suggesting that the ones that accurately reflect the observed occurrence times of event B1 B6 do not forecast event B7 better than the lower rank.

We assign a latitude and longitude to the forecast event using a robust estimate of the center of the retweeter's locations, i.e. the forecast location is the l 1-multivariate median [18] of the retweeter locations.

These local increases of the forecast event rates are compensated by decreases in other places so that the total event rate over the whole territory does not significantly change (see Appendix 2). Figure 4 Three-month forecasts of EAST, EAST R, EEPAS, and EAST ∗EEPAS models.

For this application, the Archimedes Model is unique in its ability to capture trial protocols and forecast event rates in the high-risk populations typical of CV safety RCTs.

Other monthly editors are also wrestling with the difficulty of forecasting events and moods.