Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918128168992768 |
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| author | Li, Luke |
| author_facet | Li, Luke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition Li, Luke Machine Learning 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. |
| title | Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.12565 |