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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 8 Pith papers
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HMamba, a hierarchical Mamba-based model with a decoupled cross-entropy loss, jointly performs pronunciation scoring and mispronunciation detection, reaching an MDD F1 of 63.85% on speechocean762.
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A dual-channel forecasting architecture combining channel-independent Mamba and channel-mixing linear attention is proposed, but its claimed superiority is contradicted by its own experimental table on several dataset...
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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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