Genetic protein sequence analysis based on sequence alignment techniques for time series data

Abstract

This study aims to explore the application and effectiveness of sequence comparison techniques in dealing with missing and outliers in time series data. First, the data are pre-processed by convolutional neural network (CNN) and recurrent neural networks (RNN) to remove noise and outliers. Then, time series data at different time points are compared and analysed using the comparison loss function to identify changes and differences in the data. Finally, the prediction performance of different models is evaluated using a variety of assessment metrics, and the results are compared and analysed to verify the effectiveness of the sequence comparison technique in dealing with missing and outliers. The experimental results show that the sequence comparison technique can effectively deal with missing and outliers in time series data, providing important insights for further research on the application and development of the sequence comparison technique. Future research can explore the application of sequence comparison techniques in more fields to optimize model performance and improve accuracy and stability.