# A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Prediction

arXiv: https://arxiv.org/abs/2406.03157 · HTML: https://mlsanity.com/paper/2406.03157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Community Network Auto-Regression for High-Dimensional Time Series](https://mlsanity.com/paper/2007.05521/) (2020)
- [A Sequential Variational Mode Decomposition Method](https://mlsanity.com/paper/2103.05874/) (2021)

## Built on by (most cited first, 1 of 1)

- [KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction](https://mlsanity.com/paper/2412.05421/) (2024)

## Abstract (arXiv)

Accurate prediction of financial time series is a key concern for market economy makers and investors. The article selects online store sales and Australian beer sales as representatives of non-stationary, trending, and seasonal financial time series, and constructs a new SGVMD-ARIMA combination model in a non-linear combination way to predict financial time series. The ARIMA model, LSTM model, and other classic decomposition prediction models are used as control models to compare the accuracy of different models. The empirical results indicate that the constructed combination prediction model has universal advantages over the single prediction model and linear combination prediction model of the control group. Within the prediction interval, our proposed combination model has improved advantages over traditional decomposition prediction control group models.