arXiv：cs.LG· Jierui Lei, Wenjian Zhang, Qingyi Yang, Yuyang Hong, Fangzheng Chen, Zhengbo Zhang, Haina Tang, Shiming Xiang·· 3 小时前
MIDAPN：重新审视媒体桥接时间序列预测中的身份与谱分散
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows
AI 导读
研究者提出多媒体身份感知棱镜网络（MIDAPN），一个统一时空预测骨干，通过多媒体身份感知图（MIDAG）和谱棱镜卷积（SPConv）实现跨媒体变量依赖扩展与层级时间分析。在13个"多变量"和12个"多模态"数据集上对比16个SOTA TSF模型，并在长上下文设置下与14个时间序列基础模型及融合预训练语言模型比较，MIDAPN表现出一致优势与广泛骨干兼容性。代码已开源。
正文
Abstract:Media-bridged time series forecasting is expanding to encompass traditional "multivariate" and emerging "multimodal" (e.g., through textual assistance). Existing Time Series Forecasting (TSF) models still rely on paradigm-specific relation, fusion, and temporal modules, hindering a common forecasting backbone across numerical and pre-aligned narrative-flow settings. To explore this, we propose the Multimedia Identity-Aware Prism Network (MIDAPN), a unified spatiotemporal forecasting backbone based on media-general graph adaptation and automatic temporal learning: (1) Following media pre-alignment, our Multimedia Identity-Aware Graph (MIDAG) revisits identity through static essence, dynamic behavior, and latent commonality, inducing affinities that extend variable-specific dependencies across media. Contextual Identity Modulation (CIM) further refines discriminative aggregation. (2) We develop Spectral Prism Convolution (SPConv) to automatically perform hierarchical temporal analysis, balancing coarse trends and fine-grained details. Meanwhile, its Adaptive Search Guidance configures a scale-efficient architecture for temporal-dimension reconstruction. These decoupled yet synergistic components jointly address media identity disentanglement and temporal-scale mismatch. Comprehensive evaluations involving 16 SOTA TSF models across 13 "multivariate" and 12 "multimodal" datasets, alongside targeted long-context comparisons against 14 time series foundation models and fused pretrained language models, demonstrate MIDAPN's consistent superiority and broad shared backbone compatibility. The code is available at \href{this https URL}{this https URL}.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11924 [cs.CV] |
| (or arXiv:2610.11924v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11924 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jierui Lei [view email]
[v1]
Thu, 8 Oct 2026 13:20:49 UTC (7,868 KB)
来源：arXiv：cs.LG · arxiv.org