Enhancing Time-Series Classification with InceptionResNet: Integrating Deep Learning Techniques for Improved Performance

In the domain of deep learning, several architectures have been extensively used for time series data analysis and mining, including recurrent neural network (RNN)-based architectures such as gated recurrent units (GRUs) and long short-term memory (LSTM). Despite their effectiveness, these models face challenges in analyzing long-range dependencies due to their reliance on current time-step data. To address this limitation, the integration of convolutional neural networks (CNNs) into time-series analysis has been explored, enhancing model performance and accuracy. This paper focuses on addressing these challenges by applying a modified InceptionTime model for time-series classification (TSC). Our study conducts an in-depth review and evaluation of existing deep learning techniques for TSC, exploring potential model combinations to improve accuracy. Specifically, we investigate the integration of CNN and RNN models, leveraging their strengths for both TSC and image processing tasks. Our proposed model, evaluated in the UCR-85 data set, shows significant improvements in accuracy and performance, offering a promising approach for complex time series analysis problems.