Table 1.

Mean squared error and root mean squared error of next measured glucose for DA, linear regression, and Gaussian process modeling vs. training set size. Comparing the MSE with the RMSE highlights the impacts of selecting different forecast validation metrics.^74,75.The MSE places more weight on outlier or excursion error, whereas the RMSE places more weight on errors associated with estimating the mean, an interpretation that can be seen by comparing the formulas of RMSE and MSE. The DA used here was the unscented Kalman filter without optimizing or carefully selecting parameters estimated; it adjusted the model as new data arrive so the concept of a training set for the DA is not equivalent to the training set of the other two regressions. While the DA is the best forecasting engine for this person, this is not always the case.¹⁵ Moreover, DA forecasting errors can be further reduced by at least 20% if the parameters estimated are more carefully chosen⁷⁶

	N = 5	N = 10	N = 15	N = 20	N = 25
Mean Squared Error
DA	340	340	340	340	340
Linear regression	555	425	380	385	380
Gaussian process	505	405	410	480	490
Root MSE
DA	18	18	18	18	18
Linear regression	24	21	19	20	19
Gaussian process	22	20	20	22	22