Stock Prediction Model And System Implementation Of Kernel Adaptive Filter Based On Multi-scale Decomposition

In the context of a stable and progressively developing national economy,financial investment has drawn increased attention from investors.The prediction of stock prices plays a crucial role in guiding investor behavior,as well as aiding enterprises and government agencies.In light of the continued development of artificial intelligence,traditional analytical methods have gradually given way to quantitative techniques such as machine learning and deep learning.Correspondingly,the accuracy and reliability of these models have improved.However,the stock market is a highly intricate system,and its characteristic features,including high noise,nonlinearity,and actual economic significance,make the construction of an effective and reliable stock prediction model a significant challenge.The kernel adaptive filter(KAF)is a sequential prediction model that leverages memory and error correction for improved online prediction.Compared to other machine learning models,KAF has demonstrated superior performance in predicting financial time series due to its ability to handle online prediction.However,KAF’s prediction efficiency is impacted by its sensitivity to robustness and network structure suppression.In addition,financial time series contain both long-term trends and short-term fluctuations at different scales,which can provide more informative signals for prediction models.Currently,single models do not consider multiscale information.Furthermore,there is a significant interdependence among stocks in the stock market,meaning that constituent stocks under the same industry plate are affected by common factors and exhibit similar overall trends.To address these challenges,this paper proposes a novel kernel adaptive filter stock prediction model based on multi-scale analysis(MISQ-KAF).The model integrates improved KAF and wavelet multiscale analysis techniques while considering stock interdependence.First,the original time series is decomposed into low frequency trend sequences and high frequency noise sequences through wavelet multiscale decomposition.The trend sequences are divided into input-output data pairs,and the improved KAF is successively trained.The quantization method based on difference quotient is used to determine whether to allocate new kernel units to the input data,and the pattern dictionary of input sequences and filter parameters is trained.When new input data is received,the most similar sequences in the pattern dictionary are matched according to the KL divergence,and the corresponding parameters are used to calculate the predicted values.The prediction error is fed back into the filter to update the coefficients,enabling online prediction and update of stocks.To account for the interdependence of stocks,cluster analysis is performed on constituent stocks under the same industry plate,and a collective stock pool of individual stocks with similar overall trends is obtained.Sequence training is conducted on the individual constituent stocks,and a plate pattern dictionary is obtained,providing a forecast model for all constituent stocks.Experimental results using data sets from several industry plates in the A-share market demonstrate that the proposed MISQ-KAF model has good predictive performance.The proposed MISQ-KAF model in this paper enhances the robustness of the KAF model by improving the loss function to maximum cross-correlation entropy.To suppress the growth of the radial basis function network,an improved sparse quantization method is proposed based on the difference quotient method.Additionally,the wavelet multiscale analysis algorithm is combined with the KAF algorithm to enhance the trend characteristics of the time series and reduce the impact of noise on model accuracy.By considering stock interdependence through clustering,the number of models can be effectively increased,thereby improving prediction accuracy.This paper presents the implementation of a visualization platform for stock prediction,based on the aforementioned research.The platform ofers users an interactive interface that is both convenient and concise,enabling them to carry out stock prediction tasks with ease.The proposed model presents a new approach to constructing multi-scale online stock prediction models.