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Multimodal Conditioned Diffusive Time Series Forecasting
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Diffusion models achieve remarkable success in processing images and text, and have been extended to special domains such as time series forecasting (TSF). Existing diffusion-based approaches for TSF primarily focus on modeling single-modality numerical sequences, overlooking the rich multimodal information in time series data. To effectively leverage such information for prediction, we propose a multimodal conditioned diffusion model for TSF, namely, MCD-TSF, to jointly utilize timestamps and texts as extra guidance for time series modeling, especially for forecasting. Specifically, Timestamps are combined with time series to establish temporal and semantic correlations among different data points when aggregating information along the temporal dimension. Texts serve as supplementary descriptions of time series' history, and adaptively aligned with data points as well as dynamically controlled in a classifier-free manner. Extensive experiments on real-world benchmark datasets across eight domains demonstrate that the proposed MCD-TSF model achieves state-of-the-art performance.
Forward citations
Cited by 5 Pith papers
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Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting
DiffDiff rewires diffusion forecasting so corruption gradually emphasizes second-order differences, concentrating generation on history-uncertain parts and improving forecasts on seven benchmarks.
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Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
Uncontrolled text–time-series fusion underperforms unimodal baselines; constrained fusion and a low-rank Controlled Fusion Adapter recover gains without changing the TS backbone.
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Text Reinforcement for Multimodal Time Series Forecasting
Reinforcement learning trains an LLM to generate improved text from time series, improving multimodal forecasting on Time-MMD.
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Fusing Large Language Models with Temporal Transformers for Time Series Forecasting
A gated fusion of GPT-2 semantic features and a PatchTST-style Transformer encoder improves average MSE/MAE slightly on ETT, Weather, and ILI, while losing to PatchTST on four of the six datasets.
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Diffusion Models for Time Series Forecasting: A Survey
A survey classifies diffusion-based time series forecasting models into a two-axis taxonomy by conditioning source and integration method.
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