用变分模态分解+滑动窗口提升股市预测精度
Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition
- 先用VMD分解时序数据为平滑子序列，再输入LSTM模型
- 相比原始数据，预测误差降低12.7%，波动更小
- 适合关注金融时序建模的从业者与研究者
为应对金融时间序列的复杂性，本文提出一种结合滑动窗口与变分模态分解（VMD）的预测模型。利用历史股价及市场指标构建数据集，通过VMD将非平稳时间序列分解为更平滑的子成分，提升模型适应能力。分解后的数据输入深度学习模型进行预测。实验对比了基于原始序列与经VMD处理序列训练的LSTM模型的预测效果，结果显示，使用VMD处理后的序列使预测性能更优且更稳定。
原文摘要 · Abstract (English)
To address the complexity of financial time series, this paper proposes a forecasting model combining sliding window and variational mode decomposition (VMD) methods. Historical stock prices and relevant market indicators are used to construct datasets. VMD decomposes non-stationary financial time series into smoother subcomponents, improving model adaptability. The decomposed data is then input into a deep learning model for prediction. The study compares the forecasting effects of an LSTM model trained on VMD-processed sequences with those using raw time series, demonstrating better performance and stability.
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