SPPMFN: Efficient Multimodal Financial Time-Series Prediction Network With Self-Supervised Learning
Financial time series exhibit high volatility and non-linearity, making analysis particularly challenging. Traditional statistical methods like ARIMA and GARCH struggle with non-linear data. At the same time, despite capturing complex price dynamics, machine learning and deep learning approaches oft...
Saved in:
| Main Authors: | Ningxin Li, Gang Chao, Jianke Zou, Gaozhe Jiang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11104243/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The self supervised multimodal semantic transmission mechanism for complex network environments
by: Jiajun Zou, et al.
Published: (2025-08-01) -
A Pretrained Spatio-Temporal Hypergraph Transformer for Multi-Stock Trend Forecasting
by: Yuchen Wu, et al.
Published: (2025-05-01) -
Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning
by: Pengbo Zhao, et al.
Published: (2024-01-01) -
Supervised autoencoder MLP for financial time series forecasting
by: Bartosz Bieganowski, et al.
Published: (2025-08-01) -
Cross-Sensor Self-Supervised Training and Alignment for Remote Sensing
by: Valerio Marsocci, et al.
Published: (2025-01-01)