## 1 Introduction  
Diabetes affected 537 million people with diabetes worldwide in 2021 [1], and this figure is estimated to rise to 643 million by 2030 and 783 million by 2045, which makes diabetes one of the major health challenges of the 21 st century. Type 1 diabetes (T1D) is the most severe variant of this condition, and affects about 10% of people with diabetes. In people with T1D, \(\beta\)-cells in the pancreas are destroyed by an autoimmune response, impairing or completely suppressing insulin secretion, causing blood glucose (BG) concentrations to escape the safe range of 70-180 mg/dL. Since the discovery of insulin, T1D treatment usually consists in exogenous administration of insulin, or more recently, insulin analogs, in order to avoid long-term complications due to hyperglycemia (high concentration of BG). Unfortunately, insulin overdosing may reduce BG concentration to hypoglycemic levels, which may lead to severe consequences such are comma and death when untreated. Ambulatory BG control is demanding for patients, who carry out many critical decisions and calculations per day, and are required to constantly monitor BG levels to avoid hyper- and hypoglycemia. Nowadays, glucose concentration can be measured in real time by using continuous glucose monitoring (CGM) devices [2], which are increasingly common medical devices in the treatment and supervision on T1D. However, accurate glucose predictions over long time horizons are still needed to help in preventing hyperglycemic and, especially, hypoglycemic episodes [3; 4]. Glucose prediction is important in sensor-pump integrated systems [5] or artificial pancreas (AP) systems [6; 7; 8; 9]. There are a variety of alternatives for BG prediction in T1D in the literature, such as, among others, linear empirical dynamic models, multivariate nonlinear regression techniques, extended Kalman Filters, data mining or artificial intelligence approaches [10; 11; 12]. More recently, deep learning and meta-learning [13; 14; 15] approaches have yielded very accurate predictions when trained on population data, albeit those models need to be fed exogenous data that may not be reliably available or accurate. A promising approach is the use of seasonal stochastic local models [16; 17], since these models provide reliable glucose predictions under high intra-patient variability for long prediction horizons. The rationale lies in the fact that observed behaviors in past similar scenarios can help to improve predictions at the present moment. Seasonal stochastic time series models such as SARIMA (seasonal autoregressive integrated moving average) or SARIMAX (SARIMA including eXogenous variables) have exhibited good accuracy for postprandial BG prediction accuracy over long prediction horizons [16], including challenging scenarios with a variety of meals and exercise sessions [17]. Seasonal local modeling also proved successful under real-life scenarios [18], but at least four months of data from each patient were needed to properly train the personalized system. SARIMA models have recently been proven to provide valid short term predictions in real life experimental datasets, as shown in [19], holding their ground against neural networks when predicting glucose in two different experimental benchmark datasets. Thus, it is desirable to reduce as much as possible the individual historical data needed to train these models in order to provide valid glucose predictions to the patient and clinical staff as soon as possible. The question that motivated this work is whether training from population data, composed of smaller datasets from many patients, provides accurate enough glucose predictions when used in an individual. Additionally, we investigated to what extent individual data needs can be reduced in the training process to maintain prediction accuracy.