# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 5 Evaluation
### Blood Glucose Level Prediction
#### 5.2.4 Results
We also evaluate CORAL against other existing approaches--Recursive Least Square (RLS) with the exponential decay and the sliding window forgetting algorithms and ALPaCA with the exponential decay forgetting algorithm. RLS does not use meta-learning, whereas ALPaCA employs offline meta-learning, and CORAL exploits both offline and online meta-learning. The exponential decay algorithm is presented with the three decay factors: \(\lambda=0.99,0.995\) and \(0.9975\), and the sliding window algorithm is also evaluated with the three sliding window sizes: \(n=288,576\) and \(1152\), which are equivalent to one day, two days and four days. The results are shown in Figure 8 for the stationary target profile (adult#001) and Figure 9 for the non-stationary target profile (adult#001 and adult#003). \begin{table}
\begin{tabular}{c c c} \hline \hline
**RMSE (mg/dL)** & **Offline ML Off** & **Offline ML On** \\ \hline Online ML Off & 34.81 (28.77) & 29.07 (26.88) \\ Online ML On & 32.39 & 26.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The summary of results for the BGL prediction task with the non-stationary target profile varying between adult#002 and adult#003. Numbers in the brackets are from the model without the sliding window, and the others are with the sliding window (length = 576). ML denotes meta-learning. Figure 7: The figure shows the mean squared error (MSE) with a 20-day moving average length. The x-axis shows time steps in days. All of the results use the two-day sliding window. The dotted lines are for the model without offline meta-learning. The solid lines are for the model with offline meta-learning. The blue lines are for the model without online meta-learning. The orange lines are for the model with online meta-learning. The MSE at the beginning shows the offline meta-learning improves MSE. As time progress, the model with online meta-learning results improves over the results without online meta-learning. These plots have the BGL prediction RMSE in the y-axis and the prediction time, which shows how far into the future it predicts in the x-axis. The RLS results for the stationary target Figure 7(a) shows that the RLS without any forgetting performs best, which is expected as the target is stationary. However, RLS with the exponential decay performs better for the shortest prediction time (5 min). We think this indicates that the prediction model is not yet fully trained to capture all the complex BGL behaviours, despite being trained with eighty days of data. The exponential decay algorithm forces the model to focus on the recently acquired data, and as a result, it does not generalise as well. Hence, the model becomes good at predicting the near future but not adequate for the more distant future predictions. The results of ALPaCA (b) show an excellent performance. It suggests that offline meta-learning helps learn the complex BGL behaviours. On the other hand, the results with the exponential decay algorithm do not show any benefit from offline meta-learning, and they are almost identical to the those for RLS with exponential decay. These results are expected as the exponential decay is also applied to the prior given by offline meta-learning, and it is lost after certain time steps (as discussed in Section 4.2). The results of CORAL (c) show good performance but not as well as ALPaCA without the forgetting algorithm. The target is stationary, and it requires neither online meta-learning nor a forgetting algorithm. Figure 8: Blood glucose level (BGL) prediction RMSE for the stationary target model (adult#001) with three base models—RLS, ALPaCA and CORAL. The x-axis shows its prediction time in minutes, which shows how far into the future it predicts. Figure 9 shows the results for the non-stationary target. The most relevant difference from the stationary target results is that the ALPaCA without the forgetting algorithm performs much worse as it requires a forgetting algorithm to follow the non-stationary target.