自适应归一化Mamba与多尺度趋势分解及补丁MoE编码
Adaptive Normalization Mamba with Multi Scale Trend Decomposition and Patch MoE Encoding
AI总结：
AdaMamba通过自适应归一化、多尺度趋势分解和补丁MoE编码提升时间序列预测的稳定性和准确性。
摘要：
现实环境中的时间序列预测面临显著挑战，包括非平稳性、多尺度时间模式和分布偏移，这些都会降低模型的稳定性和准确性。本研究提出AdaMamba，一种统一的预测架构，通过自适应归一化、多尺度趋势提取和上下文序列建模来解决这些挑战。AdaMamba首先通过多尺度卷积趋势提取和通道级重校准的自适应归一化块去除非平稳成分，从而实现一致的趋势消除和方差稳定。归一化后的序列随后通过上下文编码器处理，结合补丁级嵌入、位置编码和增强的Transformer层，以及专家混合前馈模块，能够高效建模长距离依赖和局部时间动态。轻量级预测头生成多时间跨度预测，而去归一化机制通过重新整合局部趋势来重建输出，以确保在变化的时间条件下具有鲁棒性。AdaMamba具有强大的表征能力，具有模块化扩展性，支持确定性预测并兼容概率扩展。其设计有效缓解了协变量偏移，并在异构数据集上增强了预测可靠性。实验评估表明，AdaMamba结合自适应归一化和专家增强的上下文建模，在传统Transformer基线中实现了稳定的改进。
英文摘要：
Time series forecasting in real world environments faces significant challenges non stationarity, multi scale temporal patterns, and distributional shifts that degrade model stability and accuracy. This study propose AdaMamba, a unified forecasting architecture that integrates adaptive normalization, multi scale trend extraction, and contextual sequence modeling to address these challenges. AdaMamba begins with an Adaptive Normalization Block that removes non stationary components through multi scale convolutional trend extraction and channel wise recalibration, enabling consistent detrending and variance stabilization. The normalized sequence is then processed by a Context Encoder that combines patch wise embeddings, positional encoding, and a Mamba enhanced Transformer layer with a mixture of experts feed forward module, allowing efficient modeling of both long range dependencies and local temporal dynamics. A lightweight prediction head generates multi horizon forecasts, and a denormalization mechanism reconstructs outputs by reintegrating local trends to ensure robustness under varying temporal conditions. AdaMamba provides strong representational capacity with modular extensibility, supporting deterministic prediction and compatibility with probabilistic extensions. Its design effectively mitigates covariate shift and enhances predictive reliability across heterogeneous datasets. Experimental evaluations demonstrate that AdaMamba's combination of adaptive normalization and expert augmented contextual modeling yields consistent improvements in stability and accuracy over conventional Transformer based baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
