## 4 Conclusions  
In previous works, seasonal local modeling proved successful in glucose prediction under realistic scenarios for long prediction horizons. However, a minimum of 4 months of data for each patient were needed to properly train the system personalized to the user. The predictive power of these previously tested individualized glucose predictors has been compared to those obtained by a single model for all patients trained using population data. The results shows that there is no loss of predictive power when population and individual approaches are compared. The population approach was easier and faster to implement and train, as only three different glucose predictors were trained for the whole cohort of patients, instead of the thirty glucose predictors that the individual approach requires. Additionally, the use of population data avoids the necessity of using specific data from a patient and transfers knowledge from a generic population to a specific patient by learning common behaviours (local models) useful for all. While the prediction accuracy metrics reported are promising, in order to extrapolate these results to a general population, these models will have to be assessed in a randomized experimental setting.  
In summary, it was observed that no significant differences were found between using 16, 12 and 8 weeks for training the population glucose predictors when using them to forecast individual data. Therefore, using an ensemble of data from diverse patients reduces the amount of data required to train models by half, without losing any predictive!ower.  
Conceptualization, J.-L.D. and J.B.; methodology, J.-L.D., A.J.L.S. and J.B.; software, A.A., J.-L.D. and J.B.; validation, A.A., J.-L.D., A.J.L.S. and J.B. formal analysis, J.-L.D., A.J.L.S. and J.B.; investigation, J.-L.D., A.J.L.S. and J.B.; resources, J.-L.D. and J.B.; data curation, A.A., J.-L.D., A.J.L.S. and J.B.; writing--original draft preparation, A.A. and J.-L.D.; writing--review and editing, J.-L.D., A.J.L.S. and J.B.; visualization, A.A., J.-L.D., A.J.L.S. and J.B.; supervision, J.-L.D. and J.B.; project administration, J.-L.D. and J.B.; funding acquisition, J.-L.D. and J.B. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Median and inter-quartile range trends in RMSE for different PHs.  
**Funding:** This work was supported by grant PID2019-107722 RB-C21 funded by MCIN/AEI/10.13039/ 501100011033, by grant CIPROM/2021/012 funded by Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital from Generalitat Valenciana, by CIBER -Consorcio Centro de Investigacion Biomedica en Red- group number CB17/08/00004, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacion and by European Union-European Regional Development Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The simulated datasets used in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.