Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models

TL;DR

This paper proposes a novel stock forecasting approach combining wavelet-based denoising with SVR and RNN-LSTM models to improve prediction accuracy on non-stationary financial data.

Contribution

It introduces the use of M-band wavelet transform for denoising stock data before applying SVR and RNN-LSTM models, enhancing prediction accuracy.

Findings

Wavelet denoising improves stock data stability.

SVR and RNN-LSTM outperform traditional methods.

Enhanced prediction accuracy leads to better financial decisions.

Abstract

The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Average. However, by utilizing the Wavelet Transform in place of the Moving Average to denoise stock signals, financial data can be smoothened and more accurately broken down. This newly transformed, denoised, and more stable stock data can be followed up by non-parametric statistical methods, such as Support Vector Regression (SVR) and Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) networks to predict future stock prices. Through the implementation of these methods, one is left with a more accurate stock forecast, and in turn, increased profits.