The encoder (Fig. ) initiates the Seq-to-Seq model in the ARCANA framework by processing historical temporal segments of the past battery life cycles. Employing a LSTM network, it is designed to capture complex, non-linear relationships and time dependencies inherent in sequence data. The encoder processes the input tensor to accommodate sequences of different lengths, employing a padding mechanism that enables the LSTM to efficiently process these sequences without being constrained by their varying lengths. Within the LSTM, the temporal data is transformed into a new tensor, constructing hidden and cell states that capture sequential information. A skip connection incorporates the initial input into the LSTM output, thus preserving crucial temporal features and stabilizing the learning process. Layer normalization, when applied to the LSTM output, not only accelerates convergence but also leads to robust performance, mitigating the challenges associated with longsequence dependencies . The encoder returns a rich latent representation of the historical data, consisting of the output tensor and the updated hidden and cell states, which are then utilized by the decoder to enable accurate forecasting in subsequent steps.