Table 3.

Notations with description.

Notation	Description
	Input time-series matrix with time steps and sensor features
	Number of time steps in each input sequence
D	Number of input features (e.g., sensor channels)
	Kernel size used in temporal convolutions
	Dilation factor in dilated convolutions
	Number of convolutional filters in the -th TCN branch
	Total number of TCN output feature maps (sum across all branches)
	Output feature representation from the TCN module
	Output from BiLSTM containing forward and backward contextual features
H	Hidden dimension of LSTM in each direction
	Forward and backward hidden states at time 't'
	Concatenated BiLSTM hidden state at time 't'
	Attention alignment score at time step 't'
	Normalized attention weight at time step 't'
	Context vector computed as the attention-weighted sum of hidden states
	Concatenated attention and hidden feature at time step 't'
	Final attention-enhanced hidden representation at time 't'
	Attention-refined sequence of hidden representations
	Output vector after Global Average Pooling (GAP)
	Mean squared error loss for prediction accuracy.
	Loss for encouraging temporal consistency across outputs
	Attention supervision loss for pattern alignment
	Total composite loss function
	Weighting coefficients for loss terms (satisfying )