Research on the Signal Denoising and Prediction Methods Based on Regression Models: Taking Three Types of Signal Waves as An Example

Signal denoising and prediction have always been a focus in the fields of electronics and communication. Prior to this, Fourier transform was commonly used for denoising. More and more researchers are seeking other better signal processing methods. This paper provides a method of using regression models for this processing and uses Python code to generate relevant data and perform regressions processing. Then this paper makes a comparison between the regression model and Fourier transform for signal denoising and prediction performance. Finally, this paper provides prospects for optimizing and improving the experimental process, as well as exploring more applications of regression models in signal processing, based on the experimental results obtained. Through research and experiments, this paper has identified the advantages and disadvantages of different regression models for processing different signal waves, and verified the feasibility of regression models for processing signals, confirming the development prospects of machine models in the field of signal processing.