Development and Comparative Analysis of Temporal Convolutional Network for Time Series Data Classification
DOI:
https://doi.org/10.52783/jns.v14.3195Keywords:
Temporal Convolutional Network, Time-series Classification, Biomedical Signal Processing, Dilated Convolutions, Deep LearningAbstract
The paper describes the development and assessment of a Temporal Convolutional Network model used for the classification
of time-series data, with focus on biomedical signal datasets. The proposed TCN uses dilated convolutions to effectively
reflect temporal dependencies. Two implementation variants are compared in terms of their performance: one with softmax
activation that is ideal for multi-class, and the other with sigmoid activation, used in case of binary classification. The basis
of comparison is the accuracy and loss computed for training and validation datasets. The findings reveal that TCN models
can accurately solve complicated classification tasks that are based on time series. The models offer a competitive alternative
to conventional deep learning.
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