# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 5 Evaluation
### Blood Glucose Level Prediction
#### 5.2.4 Results  
The first set of results for the stationary target profile (adult#001) are summarised in Table 1. The results suggest that the offline meta-learning improves a lot in the RMSE measure in all cases. Although the improvements are smaller than with offline meta-learning, online meta-learning also improves the RMSE. The best result with the sliding window is worse than the result without it. This was expected as the target is stationary. The gap between them is reduced from 4.80 mg/dL to 1.57 mg/dL by enabling online meta-learning. It suggests that the online meta-learning mitigates the degradation due to the short window size and narrows the gap. The next set of results is for the non-stationary targets varying between adult#001 and adult#003 profiles in Table 2. Without the sliding window, the results with and without offline meta-learning are similar to the previous results. It suggests that the errors are mainly due to failing to follow the target variation. With the sliding window, the results are improved except without meta-learning. It shows that the effectiveness of the sliding window approach and the meta-learning combination for the non-stationary targets and just the sliding window alone (or meta-learning alone) do not perform as well as the combination. The third set of results is for the non-stationary targets again but between adult#002 and adult#003 in Table 3. The adult#002 profile is closer to adult#003 profile than adult#001 profile; hence, these tests do not have as many variations as the previous set of the test case. The results also indicate it, as the best results with and without the sliding window are similar. Overall, the results are encouraging as in the non-stationary target case, the results with sliding window plus meta-learning are better than those without the sliding window. In the stationary target case, both results are similar. Ideally, the BGL would be controlled within a range of 70 to 180 mg/dL (this range would vary depending upon their age), and it could go up to over 300 mg/dL. Considering the ideal range of BGL, the evaluation \begin{table}
\begin{tabular}{c c c} \hline
**RMSE (mg/dL)** & **Offline ML Off** & **Offline ML On** \\ \hline Online ML Off & 44.62 (36.30) & 25.81 (21.01) \\ Online ML On & 42.25 & 22.58 \\ \hline \end{tabular}
\end{table} 
Table 1: The summary of results for the BGL prediction task with the stationary target profile (adult#001). 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. \begin{table}
\begin{tabular}{c c c} \hline
**RMSE (mg/dL)** & **Offline ML Off** & **Offline ML On** \\ \hline Online ML Off & 35.54 (30.68) & 30.78 (31.59) \\ Online ML On & 31.85 & 27.63 \\ \hline \end{tabular}
\end{table} 
Table 2: The summary of results for the BGL prediction task with the non-stationary target profile varying between adult#001 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. results for our approach show acceptable performances. However, it still has much room for improvement. Figure 7 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 simulation runs 100 days worth of data, and the plot shows up to 80 days because it is applied 20 days moving average. 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 that the offline meta-learning improves MSE. As time progresses, the results with online meta-learning (orange lines) show improvements over the results without online meta-learning (blue lines).