Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models
This is an incremental improvement for investors and traders seeking better stock predictions.
The paper tackled stock price prediction by using wavelet transforms to denoise financial data instead of moving averages, followed by SVR and RNN-LSTM models, resulting in more accurate forecasts and increased profits.
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.