Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition

Luke Li

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arXiv · 2508.12565

Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition

Luke Li

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Abstract

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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BibTeX RIS

Luke Li. 2025-08-21. Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition. https://arxiv.org/abs/2508.12565

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