Based on hypernetworks and multifractals: Deep distribution feature fusion for multidimensional nonstationary time series prediction

Yuewen Shen, Lihong Wen and Chaowen Shen

Chaos, Solitons & Fractals, 2024, vol. 182, issue C

Abstract: It is commonly assumed that time series data follows a stationary distribution in time series prediction tasks, while the reality is that distribution drift issues are widespread. This study addresses the prediction of multi-dimensional non-stationary time series by proposing a solution that incorporates feature fusion based on classical fractal theory, specifically the multifractal spectrum width, and a hyper-network model. The HFMF (Hypernetwork Feature-Fusion Multidimensional Forecasting) network is designed, and innovative deep convolution modules, Mdff (Multidimensional deep feature fusion) and Dmff (Deep multifractal feature fusion), are introduced to fuse distribution information and time series information at the hyper-layer of the hyper-network. In the experimental section, extensive experiments are conducted on five publicly available datasets, including model performance testing, ablation experiments, generalization experiments, and other experiments. The results confirm the superior performance of our method in predicting multivariate non-stationary time series, surpassing other state-of-the-art techniques.

Keywords: Multifractals; Non-stationary time series; Hypernetworks; Feature fusion (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924003631
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:182:y:2024:i:c:s0960077924003631

DOI: 10.1016/j.chaos.2024.114811

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().