LogSig-SSM: Time-Series Modelling with Multi-Scale Log-Signature Compression for State-Space Models

Excerpt

arXiv:2610.05051v1 Announce Type: cross Abstract: Time-series data are often sampled irregularly at high frequencies and exhibit long-range dependencies, which makes long-horizon modelling difficult. Continuous-time models such as neural controlled differential equations (NCDEs) and neural rough differential equations (NRDEs) can handle irregular sampling, but they scale poorly to long sequences. Selective state-space models (SSMs) such as Mamba scale linearly with sequence length, but they prov