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

arXiv: https://arxiv.org/abs/2508.12565 · HTML: https://mlsanity.com/paper/2508.12565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention](https://mlsanity.com/paper/2504.08183/) (2025)
- [A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection](https://mlsanity.com/paper/2503.21160/) (2025)
- [A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Data](https://mlsanity.com/paper/2504.00287/) (2025)

## Abstract (arXiv)

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.