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Stock price series forecasting using multi-scale modeling with boruta feature selection and adaptive denoising

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成果类型:
期刊论文
作者:
Li, Jing;Liu, Yukun;Gong, Hongfang;Huang, Xiaofei
通讯作者:
Gong, HF
作者机构:
[Huang, Xiaofei; Gong, HF; Gong, Hongfang; Li, Jing; Liu, Yukun] Changsha Univ Sci & Technol, Sch Math & Stat, Changsha 410114, Peoples R China.
通讯机构:
[Gong, HF ] C
Changsha Univ Sci & Technol, Sch Math & Stat, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Stock index prediction;Empirical mode decomposition;Signal denoising;Feature selection-Boruta;Support vector machine
期刊:
Applied Soft Computing
ISSN:
1568-4946
年:
2024
卷:
154
页码:
111365
基金类别:
CRediT authorship contribution statement Jing Li: Conceptualization, acquisition, Supervision. Yukun Liu: Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. Hongfang Gong: Formal analysis, Supervision, Writing – review & editing. Xiaofei Huang: Investigation, Methodology, Writing – review & editing.
机构署名:
本校为第一且通讯机构
院系归属:
数学与统计学院
摘要:
In recent times, predicting stock prices has garnered attention from both regulators and academic circles. However, the intricate nature of financial time -series data, with its nonlinearities, discontinuities, and sensitivity to noise, complicates the understanding and forecasting of financial movements. In our approach, we initially deploy an adaptive empirical modal decomposition on the primary data to enhance model precision. Subsequently, we sift the technical indicator data through the Boruta method, enhancing selected functionalities via an adaptive noise reduction technique. We then em...

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