## 4 Discussion  
### Input Importance  
Feature importance provides information to model users on where the learned model focuses when making a prediction. The ablation and integrated gradients methods share this function but perform this differently, leading to complementary results (Figure 4 and 5). However, a notable difference is that the integrated gradients method provides a greater sensitivity than the ablation method since all inputs are assigned an attribution, and the sign provides additional information. Despite deriving more information from attributions, it is notable that the results derived from the ablation method are less abstract. Indeed, the results from the ablations represent the log percentage change in the SMAPE error following ablation of an input, providing tangible understanding to the results. For instance, the black regions in the ablation results are absent in the integrated gradients method, showing practically where the regions of nonimportance lie in the input. Notably, the choice of the baseline is vital as this will considerably alter the results for the integrated gradients method. Subsequently, this discussion considers the baseline whilst interpreting the attributions. Given that XAI forecasting is an emerging field, it is uncertain if there is a better alternative to the baseline used in this study. The conclusions derived from the integrated gradients method are made cautiously and relative to the baseline. The RNN demonstrated the most irregularity of the results regarding input importance compared to the LSTM and GRU (Figures 4 and 5A). Notably, these irregularities presented as alternating bands of low and high importance. This banding pattern was prevalent in both the magnitudes and signs of the attributions. By considering both the model properties and understanding behind attributions, these results suggest that the function of this pattern is to fine-tune the prediction. The RNN derives most of its meaning from the last three days of input (cells 90-92), whereby all subsequent inputs alternate between adjusting the prediction up and down from the moving average, whilst these adjustments decay in magnitude. This banding pattern is also present in the permuted results (Figure 6). This behaviour suggests that the RNN is randomly adjusting predictions rather than applying focus to specific areas in the data, thus supporting the paradigm that RNNs are unable to learn long-term information. Given this information, the results suggested that the RNN model could provide comparable performance with a smaller input size. Figure 7: Feature importance derived from permutation method for RNN, LSTM, and GRUThe LSTM architecture has successfully demonstrated its ability to learn long-term patterns in data [41]. Despite this, the LSTM network in this study assigns very little importance to data preceding the 78 th cell. There are a few possibilities that could explain this. It may signal a lack of memory from the system, or it may reveal that the model is deliberately forgetting long-term information in order to improve the prediction. The LSTM ablation errors show similarities with those of both the GRU and RNN. Similar to the RNN, the LSTM displays a considerable noise surrounding the most recent inputs but lacks a distinctive banding pattern. Furthermore, the LSTM network demonstrates the spectrum decay of importance present in the GRU network, particularly between the 76-84 th day. This decay suggests that this LSTM model displays the ability to learn, as this decay signals the network is retaining past information through the transduction of the cell state through the network. However, the LSTM long-term memory retention appears to be less critical in prediction than in the GRU. A possible reason is that the sequence length (92) may be insufficient for the LSTM to learn long-term patterns adequately. Given that the LSTM performs better with longer and more complex sequences, the LSTM may rely more on its granularity in remembering information when provided a smaller input size [42].