Fusion of Wavelet Decomposition and N-BEATS for Improved Stock Market Forecasting

Stock market forecasting is one of the most exciting areas of time series forecasting both for the industry and academia. Stock market is a complex, non-linear and non-stationary system with many governing factors and noise. Some of these factors can be quantified and modeled, whereas some factors possess a random walk behavior making the process of forecasting challenging. Various statistical methods, machine learning, and deep learning techniques are prevalent in stock market forecasting. Recently, there has been a paradigm shift towards hybrid models, showing some promising results. In this paper, we propose a technique that combines a recently proposed deep learning architecture N-BEATS with wavelet transformation for improved forecasting of future prices of stock market indices. This work uses daily time series data from five stock market indices, namely NIFTY 50, Dow Jones Industrial Average (DJIA), Nikkei 225, BSE SENSEX, and Hang Seng Index (HSI), for the experimental studies to compare the proposed technique with some traditional deep learning techniques. The empirical findings suggest that the proposed architecture has high accuracy as compared to some traditional time series forecasting methods and can improve the forecasting of non-linear and non-stationary stock market time series.