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Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
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Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
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Long-term time series forecasting (LTSF) provides longer insights into future trends and patterns. Over the past few years, deep learning models especially Transformers have achieved advanced performance in LTSF tasks. However, LTSF faces inherent challenges such as long-term dependencies capturing and sparse semantic characteristics. Recently, a new state space model (SSM) named Mamba is proposed. With the selective capability on input data and the hardware-aware parallel computing algorithm, Mamba has shown great potential in balancing predicting performance and computational efficiency compared to Transformers. To enhance Mamba's ability to preserve historical information in a longer range, we design a novel Mamba+ block by adding a forget gate inside Mamba to selectively combine the new features with the historical features in a complementary manner. Furthermore, we apply Mamba+ both forward and backward and propose Bi-Mamba+, aiming to promote the model's ability to capture interactions among time series elements. Additionally, multivariate time series data in different scenarios may exhibit varying emphasis on intra- or inter-series dependencies. Therefore, we propose a series-relation-aware decider that controls the utilization of channel-independent or channel-mixing tokenization strategy for specific datasets. Extensive experiments on 8 real-world datasets show that our model achieves more accurate predictions compared with state-of-the-art methods.
Forward citations
Cited by 7 Pith papers
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SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting
SPDM is a geometry-aware state-space model that projects covariance matrices onto the SPD manifold tangent space and uses geometric gating to modulate SSM parameters for improved multivariate time series forecasting.
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GeoCert: Certified Geometric AI for Reliable Forecasting
GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
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FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
FMMVCC combines Mamba-based encoders with multi-view contrastive learning and fuzzy clustering to achieve state-of-the-art univariate time series clustering with linear computational complexity.
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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.
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HyBDM: Multi-Scale Hybrid Experts for Time Series Forecasting with Bidirectional Dependency Modeling
HyBDM combines a Mamba-style global-pattern expert with a local window transformer and a learned router to forecast multivariate time series, reporting state-of-the-art results on six benchmarks.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
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