Stock market time series forecasting using comparative machine learning algorithms

Abstract

Stock market trends prediction and their assessment have always been leading topics due to the market’s extreme volatility and chaotic nature. Determining the stock market possesses an abstract representation of possession over enterprises and organizations, more commonly known as “stock.” This kind of assessment is generally accepted as the foundation of market conduct is not provided with as many shares as needed admitted to financial failure. Foreseeing the performance of a single business on stock markets is a precarious one, as values of stocks are subjected to constant change. Nonetheless, viewing the stock market’s behavior in retrospect and distributing investor expectations regarding future stock market values is a beneficial approach. Different models have been proposed in the last few years, and there is a saturation of work to compare efficiency, accuracy or robustness for identifying which model works best on different applications. In this paper, a detailed comparative study of different machine learning algorithms for stock market time series prediction has been shown. It provides baselines with widely used algorithms such as Linear Regression and Support Vector Machines, to state-of-the-art methods including Long Short-Term Memory networks, Convolutional Neural Networks and Transformer-based architectures.