TriTS: Time Series Forecasting from a Multimodal Perspective

• URL: http://arxiv.org/abs/2604.16748v1
• Date: Fri, 17 Apr 2026 23:43:45 GMT
• Title: TriTS: Time Series Forecasting from a Multimodal Perspective
• Abstract summary: Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology.<n>We propose TriTS, a novel cross-modal disentanglement framework that projects 1D time series into time, frequency, and 2D-vision spaces.<n>TriTS achieves state-of-the-art (SOTA) performance, fundamentally outperforming existing vision-based forecasters by drastically reducing both parameter count and inference.
• Score: 8.124083509364981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Series Forecasting (LTSF) remains a significant challenge because real-world signals contain highly entangled temporal dynamics that are difficult to fully capture from a purely 1D perspective. To break this representation bottleneck, we propose TriTS, a novel cross-modal disentanglement framework that projects 1D time series into orthogonal time, frequency, and 2D-vision spaces.To seamlessly bridge the 1D-to-2D modality gap without the prohibitive $O(N^2)$ computational overhead of Vision Transformers (ViTs), we introduce a Period-Aware Reshaping strategy and incorporate Visual Mamba (Vim). This approach efficiently models cross-period dependencies as global visual textures while maintaining linear computational complexity. Complementing this, we design a Multi-Resolution Wavelet Mixing (MR-WM) module for the frequency modality, which explicitly decouples non-stationary signals into trend and noise components to achieve fine-grained time-frequency localization. Finally, a streaming linear branch is retained in the time domain to anchor numerical stability. By dynamically fusing these three complementary representations, TriTS effectively adapts to diverse data contexts. Extensive experiments across multiple benchmark datasets demonstrate that TriTS achieves state-of-the-art (SOTA) performance, fundamentally outperforming existing vision-based forecasters by drastically reducing both parameter count and inference latency.

Related papers

• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection [53.191369031661885]
  Unsupervised time series anomaly detection aims to build a model for identifying abnormal timestamps without assuming the availability of annotations.<n>We present Learnable Fusion of Tri-view Tokens (LEFT), a unified unsupervised TSAD framework that models anomalies as inconsistencies across complementary representations.<n>Experiments on real-world benchmarks show that LEFT yields the best detection accuracy against SOTA baselines, while achieving a 5x reduction on FLOPs and 8x speed-up for training.
  arXiv  Detail & Related papers  (2026-02-09T13:33:49Z)
• DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters [50.43534351968113]
  Existing generative time series models do not address the multi-dimensional properties of time series data well.<n>Inspired by Multimodal Diffusion Transformers that integrate textual guidance into video generation, we propose Diffusion Transformers for Time Series (DiTS)
  arXiv  Detail & Related papers  (2026-02-06T10:48:13Z)
• DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series [22.29889788385778]
  Multi-dimensional time series anomaly (MTSAD) aims to accurately identify and localize complex abnormal patterns in large-scale industrial control systems.<n>Existing approaches excel in recognizing distinct patterns under low representations, but fail to robustly capture long-range dependencies when learning from the high-dimensional time series.
  arXiv  Detail & Related papers  (2025-12-14T07:40:23Z)
• T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion [0.4915744683251151]
  T3Time is a novel trimodal framework consisting of time, spectral, and prompt branches.<n>It learns prioritization between temporal and spectral features based on the prediction horizon.<n>Our model consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-06T09:31:44Z)
• VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detection [42.694234312755285]
  Time Series Anomaly Detection (TSAD) is essential for uncovering rare and potentially harmful events in unlabeled time series data.<n>We introduce VISTA, a training-free, unsupervised TSAD algorithm designed to overcome these challenges.
  arXiv  Detail & Related papers  (2025-04-03T11:20:49Z)
• Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning [9.205228068704141]
  In real-world applications, Cyber-Physical Systems often expand as new sensors are, increasing variables in MTSF.<n>This task presents unique challenges, specifically (1) handling inconsistent data caused by adding new variables, and (2) addressing imbalanced-temporal learning.<n>To address these challenges, we propose STEV, a flexible-temporal forecasting framework.
  arXiv  Detail & Related papers  (2025-02-21T08:43:26Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification [53.55504611255664]
  FormerTime is a hierarchical representation model for improving the classification capacity for the multivariate time series classification task. It exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism.
  arXiv  Detail & Related papers  (2023-02-20T07:46:14Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.