[
    {
        "id": "osp-16382",
        "type": "article-journal",
        "title": "Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows",
        "author": [
            {
                "family": "Lei",
                "given": "Jierui"
            },
            {
                "family": "Zhang",
                "given": "Wenjian"
            },
            {
                "family": "Yang",
                "given": "Qingyi"
            },
            {
                "family": "Hong",
                "given": "Yuyang"
            },
            {
                "family": "Chen",
                "given": "Fangzheng"
            },
            {
                "family": "Zhang",
                "given": "Zhengbo"
            },
            {
                "family": "Tang",
                "given": "Haina"
            },
            {
                "family": "Xiang",
                "given": "Shiming"
            }
        ],
        "URL": "https://omanscience.com/en/articles/revisiting-identity-and-spectra-dispersion-in-media-bridged-time-series-forecasting-linking-multivariate-signals-and-narrative-flows",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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{https://github.com/leijieruilq/MIDAPN/tree/main}{https://github.com/MIDAPN}."
    }
]