One of the vital tasks in Time Series analysis is classifying time series data. This has attracted considerable interest and huge efforts in the past decades. This is still a challenging problem because of the complexity and nature of time series data. They are huge, complex, and constantly updated. There is an increasing demand to extract knowledge from time-series data. The time series has a natural feature that helps humans to visualize the structure of data. Increased interest in studying this particular data type has been sparked by the growth of recorded data, leading to a wealth of unique techniques for encoding, indexing, grouping, and categorising time series, among other tasks. To enhance the performance of traditional feature-based approaches, deep learning techniques were explored. Deep Neural Networks have revolutionized computer vision with their innovative deeper architec-tures like Residual Neural Networks and Convolutional Neural Networks. In this paper, the proposed deep learning architecture improves the performance of Residual Network for time series classification.
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