Versatile time-window sliding machine learning techniques for stock market forecasting

Abstract

Abstract The stock market which is a critical instrument in the modern financial system consistently attracts a significant number of individuals and financial institutions. The amalgamation of machine learning with stock forecasting has seen increased interest due to the rising prominence of machine learning. Among numerous machine learning models, the long short-term memory (LSTM) is favored by many researchers for its superior performance in long time series. This paper presents an innovative approach that integrates LSTM with versatile sliding window techniques to enhance prediction results and training performance. Moreover, the attached experiments incorporate convolutional filters and combined bivariate performance measures, which are invaluable methodologies for enhancing stock forecasting systems. These significant contributions are expected to be beneficial for researchers operating within this domain.

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