decoder models 8 , allowing the decoder to focus on the most relevant parts of historical data. The attention mechanism then computes a context vector associated with the encoder's output, which highlights the encoder sequences with the highest relevance to the current decoding task. This context vector, combined with the current input, forms a feature-rich tensor that is subsequently processed by a LSTM layer. Post-LSTM, the output layer is passed through a fully connected layer with a leaky ReLU activation function, crucial in maintaining network stability, and enhanced with a dropout layer placed to reduce overfitting risks. The culmination of this process is a decoder that generates forecasts for the 0.1, 0.5, and 0.9 quantiles. These provide a probabilistic range indicative of the inherent uncertainty and offer a statistical interpretation of the potential future states of the degradation profile.