Channel-Dependent State Space Model for Multivariate Time Series Forecasting
Quick summary
arXiv:2609.36453v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) is critical across many real-world domains. Existing deep learning approaches fall into two paradigms with distinct limitations: channel-independent (CI) methods unconditionally ignore cross-variable dependencies and model only temporal dynamics, while channel-dependent (CD) methods consider both but typically rely on architectural compromises to mitigate overfitting and computational overhead. We therefore propose Chameleon, a specialized CD state space model (SSM) that enables data-dependent, fine-g
Key takeaways
- arXiv:2609.36453v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) is critical across many real-world domains.
- Existing deep learning approaches fall into two paradigms with distinct limitations: channel-independent (CI) methods unconditionally ignore cross-variable dependencies and model only temporal dynamics, while channel-dependent (CD) methods consider both but typically rely on architectural compromises to mitigate overfitting and computational overhead.
- We therefore propose Chameleon, a specialized CD state space model (SSM) that enables data-dependent, fine-g
Why it matters
“Channel-Dependent State Space Model for Multivariate Time Series Forecasting” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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