The paper "Is Mamba Effective for Time Series Forecasting?" by Zihan Wang et al. explores the effectiveness of Mamba, a selective state space model, in time series forecasting (TSF). The authors highlight the limitations of Transformer-based models, which suffer from quadratic computational complexity, leading to high costs and low efficiency in real-world applications. In contrast, Mamba maintains near-linear complexity while effectively capturing hidden patterns in time series data. The proposed model, Simple-Mamba (S-Mamba), integrates Mamba's selective mechanism with a Feed-Forward Network to extract inter-variate correlations and temporal dependencies. Experiments on thirteen public datasets from various domains, including traffic, electricity, weather, and finance, demonstrate that S-Mamba achieves superior performance with low computational overhead. The study also includes extensive experiments to evaluate Mamba's potential in TSF tasks, showing that it can outperform advanced Transformer models and exhibit robust generalization capabilities. The authors conclude that Mamba has significant potential to enhance TSF tasks by balancing performance and computational efficiency.The paper "Is Mamba Effective for Time Series Forecasting?" by Zihan Wang et al. explores the effectiveness of Mamba, a selective state space model, in time series forecasting (TSF). The authors highlight the limitations of Transformer-based models, which suffer from quadratic computational complexity, leading to high costs and low efficiency in real-world applications. In contrast, Mamba maintains near-linear complexity while effectively capturing hidden patterns in time series data. The proposed model, Simple-Mamba (S-Mamba), integrates Mamba's selective mechanism with a Feed-Forward Network to extract inter-variate correlations and temporal dependencies. Experiments on thirteen public datasets from various domains, including traffic, electricity, weather, and finance, demonstrate that S-Mamba achieves superior performance with low computational overhead. The study also includes extensive experiments to evaluate Mamba's potential in TSF tasks, showing that it can outperform advanced Transformer models and exhibit robust generalization capabilities. The authors conclude that Mamba has significant potential to enhance TSF tasks by balancing performance and computational efficiency.