Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
arXiv:2602.23663v1 Announce Type: new Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities inherent in the tensor hinder the realization of rich representations. In this paper, we propose a novel representation learning method designed specifically for TTS, namely MoST. Specifically, MoST uses a tensor slicing approach to reduce the complexity of the TTS structure and learns representations that can be disentangled into individual non-temporal modes. Each representation captures mode-specific features, which are the relationship between variables within the same mode, and mode-invariant features, which are in common in representations of different modes. We employ a contrastive learning framework to learn parameters; the loss function comprises two parts intended to learn representation in
arXiv:2602.23663v1 Announce Type: new Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities inherent in the tensor hinder the realization of rich representations. In this paper, we propose a novel representation learning method designed specifically for TTS, namely MoST. Specifically, MoST uses a tensor slicing approach to reduce the complexity of the TTS structure and learns representations that can be disentangled into individual non-temporal modes. Each representation captures mode-specific features, which are the relationship between variables within the same mode, and mode-invariant features, which are in common in representations of different modes. We employ a contrastive learning framework to learn parameters; the loss function comprises two parts intended to learn representation in a mode-specific way and mode-invariant way, effectively exploiting disentangled representations as augmentations. Extensive experiments on real-world datasets show that MoST consistently outperforms the state-of-the-art methods in terms of classification and forecasting accuracy. Code is available at https://github.com/KoheiObata/MoST.
Executive Summary
This study presents MoST, a novel representation learning method for multi-mode tensor time series. MoST employs a tensor slicing approach to reduce complexity and learns disentangled representations that capture mode-specific and mode-invariant features. A contrastive learning framework is used to learn parameters, and the method is evaluated on real-world datasets, demonstrating superior performance to state-of-the-art methods. The method's ability to learn disentangled representations effectively exploits data augmentations, leading to improved classification and forecasting accuracy. The study's findings have significant implications for applications involving tensor time series, such as search engines and environmental monitoring systems.
Key Points
- ▸ MoST learns disentangled representations that capture mode-specific and mode-invariant features.
- ▸ The method employs a tensor slicing approach to reduce complexity.
- ▸ Contrastive learning is used to learn parameters, taking advantage of data augmentations.
Merits
Strength in Representation Learning
MoST's ability to learn disentangled representations effectively captures mode-specific and mode-invariant features, leading to improved classification and forecasting accuracy.
Flexibility in Tensor Time Series Analysis
The method's tensor slicing approach allows for flexibility in handling complex tensor structures, making it suitable for various applications involving tensor time series.
Demerits
Limited Generalizability to Other Domains
While MoST demonstrates impressive performance on real-world datasets, its applicability to other domains, such as those with different tensor structures, remains uncertain.
Computational Complexity
The method's reliance on contrastive learning and tensor slicing may introduce computational complexity, potentially limiting its adoption in resource-constrained settings.
Expert Commentary
MoST presents a novel and effective approach to representation learning for tensor time series. The method's ability to learn disentangled representations and exploit data augmentations is a significant advancement in the field. However, its limited generalizability to other domains and potential computational complexity require careful consideration. The study's findings have significant implications for applications involving tensor time series and can inform policy decisions related to data collection and analysis in these domains.
Recommendations
- ✓ Future studies should investigate MoST's applicability to other domains and its performance in resource-constrained settings.
- ✓ Development of explainability and interpretability techniques for MoST's disentangled representations can provide valuable insights into the relationships between variables within each mode.