MambaTSC: Towards Robust Time Series Completion via Multi-scale Temporal Enhancement and Score-Gated Graph Modeling

Multivariate time series imputation is vital for domains such as meteorology, finance, and transportation. However, most existing approaches employ a separated imputation-then-forecasting pipeline, which struggles to model complex spatio-temporal dependencies effectively and tends to incur significant error accumulation, especially in scenarios with high missing rates. To address these challenges, we propose Mamba-based Time Series Completer (MambaTSC), an end-to-end framework that integrates time series imputation and forecasting within a unified state space modeling architecture. Specifically, we first devise a Multi-Scale Temporal Enhancement Mamba (MS-Mamba) module, which augments state space modeling with multi-scale temporal feature extraction, enabling the capture of both short-term fluctuations and long-term trends. Additionally, we develop a Score-Gated Graph Convolutional Network (SGCN) that dynamically adjusts information propagation based on observation reliability, thereby improving spatio-temporal representation learning. Extensive experimental results on three real-world datasets show that our proposed MambaTSC achieves state-of-the-art performance, demonstrating superior effectiveness and robustness for time series with substantial missing data.