MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting

Understand

In recent years, Transformers have become the de-facto architecture for long-term time series forecasting (LTSF), yet they face challenges associated with the self-attention mechanism, including quadratic complexity and permutation-invariant bias.

• This raises an important question: \emph{do we truly need self-attention to model long-range dependencies in LTSF?} To address this, we propose MambaTS, a linear-scan-based framework that models global dependencies across time and variables via structured dependency modeling.
• Since explicit variable dependency structures are often unknown, we introduce Variable-Aware Scan along Time (VAST), which learns inter-variable relationships during training and determines an optimal scan order via a shortest-path-based decoding strategy during inference.
• MambaTS employs the latest Mamba model as its backbone.