ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
The problem of Time-series Forecasting is generally addressed by recurrent, Transformer-based and the recently proposed Mamba-based architectures. However, existing architectures generally process their input at a single temporal scale, which may be sub-optimal for many tasks where information changes over multiple time scales. In this paper, we introduce a novel architecture called Multi-scale Mamba (ms-Mamba) to address this gap. ms-Mamba incorporates multiple temporal scales by using multiple Mamba blocks with different sampling rates ($\Delta$s). Our experiments on many benchmarks demonstrate that ms-Mamba outperforms state-of-the-art approaches, including the recently proposed Transformer-based and Mamba-based models.
Code (0)
등록된 구현이 없습니다.
Tasks
MambaTime SeriesTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
Time series prediction, a crucial task across various domains, faces significant challenges due to the inherent complexities of time series data, including non-stationarity, multi-scale periodicity, and transient dynamic…
Time Series PredictionUmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba
Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art methods predominantly rely on Transformer ar…
DecoderMambaMultivariate Time Series ForecastingState Space Models+2ASGMamba: Adaptive Spectral Gating Mamba for Multivariate Time Series Forecasting
Long-term multivariate time series forecasting (LTSF) plays a crucial role in various high-performance computing applications, including real-time energy grid management and large-scale traffic flow simulation. However, …
Multivariate Time Series ForecastingFourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection
Time-series anomaly detection plays a critical role in numerous real-world applications, including industrial monitoring and fault diagnosis. Recently, Mamba-based state-space models have shown remarkable efficiency in l…
Anomaly DetectionFault DiagnosisDeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis
Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications. Within this realm, Transformers have emerged as the predominant architecture due to th…
Computational EfficiencyTime Series AnalysisAnomaly Detection