TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
Md Atik Ahamed Qiang Cheng
Long-term time-series forecasting remains challenging due to the difficulty
in capturing long-term dependencies, achieving linear scalability, and
maintaining computational efficiency. We introduce TimeMachine, an innovative
model that leverages Mamba, a state-space model, to capture long-term
dependencies in multivariate time series data while maintaining linear
scalability and small memory footprints. TimeMachine exploits the unique
properties of time series data to produce salient contextual cues at
multi-scales and leverage an innovative integrated quadruple-Mamba architecture
to unify the handling of channel-mixing and channel-independence situations,
thus enabling effective selection of contents for prediction against global and
local contexts at different scales. Experimentally, TimeMachine achieves
superior performance in prediction accuracy, scalability, and memory
efficiency, as extensively validated using benchmark datasets. Code
availability: https://github.com/Atik-Ahamed/TimeMachine