Long-term time series forecasting (LTSF) provides longer insights into future
trends and patterns. In recent years, deep learning models especially
Transformers have achieved advanced performance in LTSF tasks. However, the
quadratic complexity of Transformers rises the challenge of balancing
computaional efficiency and predicting performance. 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 can well
capture long-term dependencies while maintaining linear computational
complexity. Mamba has shown great ability for long sequence modeling and is a
potential competitor to Transformer-based models in LTSF. In this paper, we
propose Bi-Mamba4TS, a bidirectional Mamba for time series forecasting. To
address the sparsity of time series semantics, we adopt the patching technique
to enrich the local information while capturing the evolutionary patterns of
time series in a finer granularity. To select more appropriate modeling method
based on the characteristics of the dataset, our model unifies the
channel-independent and channel-mixing tokenization strategies and uses a
series-relation-aware decider to control the strategy choosing process.
Extensive experiments on seven real-world datasets show that our model achieves
more accurate predictions compared with state-of-the-art methods.

提出了一种名为 Bi-Mamba4TS 的双向 Mamba 模型，通过采用补丁技术丰富了局部信息并精细捕捉时间序列的演化模式，同时结合数据集特征选择更合适的建模方法，实验证明该模型在七个真实数据集上相较于现有方法获得了更准确的预测结果。