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MICA: Multivariate Infini Compressive Attention for Time Series Forecasting

arXiv:2604.06473v1 Announce Type: new Abstract: Multivariate forecasting with Transformers faces a core scalability challenge: modeling cross-channel dependencies via attention compounds attention's quadratic sequence complexity with quadratic channel scaling, making full cross-channel attention impractical for high-dimensional time series. We propose Multivariate Infini Compressive Attention (MICA), an architectural design to extend channel-independent Transformers to channel-dependent forecasting. By adapting efficient attention techniques from the sequence dimension to the channel dimension, MICA adds a cross-channel attention mechanism to channel-independent backbones that scales linearly with channel count and context length. We evaluate channel-independent Transformer architectures with and without MICA across multiple forecasting benchmarks. MICA reduces forecast error over its channel-independent counterparts by 5.4% on average and up to 25.4% on individual datasets, highlight

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Willa Potosnak, Nina \.Zukowska, Micha{\l} Wili\'nski, Dan Howarth, Ignacy St\k{e}pka, Mononito Goswami, Artur Dubrawski · · 1 min read · 17 views
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arXiv:2604.06473v1 Announce Type: new Abstract: Multivariate forecasting with Transformers faces a core scalability challenge: modeling cross-channel dependencies via attention compounds attention's quadratic sequence complexity with quadratic channel scaling, making full cross-channel attention impractical for high-dimensional time series. We propose Multivariate Infini Compressive Attention (MICA), an architectural design to extend channel-independent Transformers to channel-dependent forecasting. By adapting efficient attention techniques from the sequence dimension to the channel dimension, MICA adds a cross-channel attention mechanism to channel-independent backbones that scales linearly with channel count and context length. We evaluate channel-independent Transformer architectures with and without MICA across multiple forecasting benchmarks. MICA reduces forecast error over its channel-independent counterparts by 5.4% on average and up to 25.4% on individual datasets, highlighting the importance of explicit cross-channel modeling. Moreover, models with MICA rank first among deep multivariate Transformer and MLP baselines. MICA models also scale more efficiently with respect to both channel count and context length than Transformer baselines that compute attention across both the temporal and channel dimensions, establishing compressive attention as a practical solution for scalable multivariate forecasting.

Executive Summary

The paper introduces Multivariate Infini Compressive Attention (MICA), a novel architectural design addressing the scalability limitations of Transformers in high-dimensional multivariate time series forecasting. MICA integrates a cross-channel attention mechanism into existing channel-independent Transformer backbones, achieving linear scalability with both channel count and context length. This is accomplished by adapting efficient attention techniques from the temporal to the channel dimension. Empirical evaluations demonstrate that MICA significantly reduces forecast error compared to its channel-independent counterparts, outperforming deep multivariate Transformer and MLP baselines. The study underscores the critical role of explicit cross-channel modeling for improved accuracy and computational efficiency in large-scale forecasting tasks.

Key Points

  • ▸ MICA extends channel-independent Transformers to incorporate channel-dependent forecasting.
  • ▸ It employs compressive attention techniques to achieve linear scalability in the channel dimension, addressing a key limitation of traditional Transformers.
  • ▸ MICA significantly improves forecasting accuracy (average 5.4%, up to 25.4%) over channel-independent models.
  • ▸ Models with MICA outperform existing deep multivariate Transformer and MLP baselines in benchmarks.
  • ▸ The proposed architecture demonstrates superior scaling efficiency with respect to both channel count and context length.

Merits

Addresses a Critical Scalability Challenge

Successfully tackles the quadratic complexity barrier of full cross-channel attention in high-dimensional time series, making advanced Transformer models practical for real-world applications.

Demonstrates Significant Performance Gains

Empirical results show substantial reductions in forecast error and superior performance against strong baselines, validating the effectiveness of explicit cross-channel modeling.

Architectural Modularity and Extensibility

MICA is designed as an add-on to existing channel-independent backbones, allowing for flexible integration and leveraging of established Transformer architectures.

Strong Empirical Validation

Evaluated across multiple forecasting benchmarks, providing robust evidence for its efficacy and efficiency.

Demerits

Novelty of Compressive Attention

While adapted to the channel dimension, the core 'compressive attention' mechanism itself might not be entirely novel, building upon existing efficient attention literature.

Interpretability of Cross-Channel Attention

The abstract does not detail the interpretability aspects of the learned cross-channel dependencies, which can be crucial for domain experts.

Computational Overhead of Added Mechanism

While scaling linearly, the abstract does not explicitly quantify the absolute computational overhead added by the MICA module compared to a purely channel-independent model, beyond relative scaling efficiency.

Expert Commentary

This paper presents a compelling solution to a long-standing challenge in multivariate time series forecasting: the quadratic complexity of cross-channel attention in Transformer architectures. By ingeniously adapting compressive attention from the temporal to the channel dimension, MICA offers a practical and scalable approach. The reported performance gains, especially the significant error reduction and top rankings against baselines, are highly persuasive. From a methodological standpoint, the modular design of MICA, allowing integration into existing channel-independent backbones, is a significant strength, promoting wider adoption and experimentation. While the core concept of 'compressive attention' has precedents, its application and demonstrated efficacy in the channel dimension for high-dimensional time series forecasting represent a genuine advancement. Future work might fruitfully explore the interpretability of MICA's cross-channel attention mechanisms, which could provide critical insights for domain experts and foster greater trust in AI-driven forecasts, particularly in regulated industries.

Recommendations

  • ✓ Publish the full paper detailing the specific compressive attention mechanisms employed and a thorough ablation study.
  • ✓ Investigate the interpretability of the cross-channel attention weights to provide insights into learned dependencies.
  • ✓ Benchmark MICA against non-Transformer based state-of-the-art models (e.g., advanced deep learning models, statistical hybrid models) to provide a broader comparison.
  • ✓ Explore the robustness of MICA to noisy or incomplete multivariate time series data.

Sources

Original: arXiv - cs.LG

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