VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
This addresses the challenge of ignoring cross-channel dependencies in time series forecasting models, offering an efficient solution for applications in domains like finance or IoT, though it is incremental as it builds on existing multimodal and vision model approaches.
The paper tackles the problem of capturing cross-channel dependencies in multivariate time series forecasting by proposing VIFO, which renders time series into images for feature extraction using a pre-trained large vision model and fuses these with time series representations, achieving competitive performance on multiple benchmarks with only 7.45% of parameters trained.
Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Concurrently, existing multimodal approaches have not fully exploited the power of large vision models (LVMs) to interpret spatiotemporal data. Additionally, there remains significant unexplored potential in leveraging the advantages of information extraction from different modalities to enhance time series forecasting performance. To address these gaps, we propose the VIFO, a cross-modal forecasting model. VIFO uniquely renders multivariate time series into image, enabling pre-trained LVM to extract complex cross-channel patterns that are invisible to channel-independent models. These visual features are then aligned and fused with representations from the time series modality. By freezing the LVM and training only 7.45% of its parameters, VIFO achieves competitive performance on multiple benchmarks, offering an efficient and effective solution for capturing cross-variable relationships in