TIDES: Implicit Time-Awareness in Selective State Space Models
📰 ArXiv cs.AI
Learn how TIDES enhances selective state space models with implicit time-awareness for better handling of irregular time series data
Action Steps
- Read the TIDES paper to understand the concept of implicit time-awareness in selective state space models
- Implement TIDES in your existing state space model framework to handle irregular time series data
- Compare the performance of your model with and without TIDES on a benchmark dataset
- Apply TIDES to your specific problem domain, such as financial or weather forecasting
- Evaluate the effectiveness of TIDES in capturing complex temporal relationships in your data
Who Needs to Know This
Researchers and engineers working on time series modeling and state space models can benefit from this article to improve their models' performance on irregularly sampled data
Key Insight
💡 TIDES preserves the physical meaning of the time discretization step, allowing for more accurate modeling of irregularly sampled data
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📊 TIDES enhances state space models with implicit time-awareness for better irregular time series handling #timeseries #AI
Key Takeaways
Learn how TIDES enhances selective state space models with implicit time-awareness for better handling of irregular time series data
Full Article
Title: TIDES: Implicit Time-Awareness in Selective State Space Models
Abstract:
arXiv:2605.09742v1 Announce Type: cross Abstract: Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step $\Tilde{\Delta}$ a learned function of the input. However, in doing so, $\Tilde{\Delta}$ ceases to represent a physical sampling interval, limiting its irregular time series modeling capability. Continuous-time SSMs, such as S5, preserve the physical meaning of $\Tilde{\Delta}$ and handle irregular timestamps natively (
Abstract:
arXiv:2605.09742v1 Announce Type: cross Abstract: Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step $\Tilde{\Delta}$ a learned function of the input. However, in doing so, $\Tilde{\Delta}$ ceases to represent a physical sampling interval, limiting its irregular time series modeling capability. Continuous-time SSMs, such as S5, preserve the physical meaning of $\Tilde{\Delta}$ and handle irregular timestamps natively (
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