A new multivariate decomposition-ensemble approach with denoised neighborhood rough set for stock price forecasting over time-series information system

The uncertainty of the stock market is the foundation for investors to obtain returns. Driven by interests, stock price forecasting has become a research hotspot. However, as the high latitude, highly volatile, and noisy, forecasting the stock prices has become a highly challenging task. The existing stock price forecasting methods only study low latitude data, which is unable to reflect the cumulative effect of multiple factors on stock price. To effectively address the high latitude, high volatility, and noise of stock price, a time-series information system (TSIS) forecasting approach for stock price is proposed. Aiming at dynamically depicting the real-world decision-making scenarios from a finer granularity, the TSIS is constructed based on the information systems. Then, a denoised neighborhood rough set (DNRS) model based on the TSIS is proposed by local density factor to achieve the purpose of feature selection, which can weaken the impact of noise on sample data. Subsequently, the multivariate empirical mode decomposition (MEMD) and multivariate kernel extreme learning machine (MKELM) are employed to decompose and forecast. Finally, the proposed TSIS forecasting approach is applied to stock price. Experimental results show that the TSIS forecasting approach for stock price has excellent performance and can be provided in the quantitative trading of stock market.