Open Access

Niels Schneider, Lermer Matthias, Christoph Reich

• Time series Classification is a vital task across various domains such as finance, healthcare, and environmental science. Recurrent Neural Networks in combination with Convolutional Neural Networks have emerged as powerful tools for Time series classification due to their ability to capture temporal dependencies. Long Short-Term Memory and Gated Recurrent Unit networks, along with their bidirectional variants have been widely employed for Time series tasks, but a comparison of these architectures under different hyperparameter configurations has not yet been analysed in detail. This paper fills the gap and provides a comprehensive comparison of these four architectures. We conduct experiments on two datasets representing a healthcare and industrial domain to evaluate there performance in terms of Classification accuracy, training time, and model complexity. The results of our experiments provide insights into the strengths and weaknesses of each architecture, aiding practitioners in selecting the most suitable model for their specific tasks. The superiority of the GRU architecture was demonstrated both in terms of learning speed and accuracy.