让LSTM能处理不确定性，提升时间序列预测精度。
StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting
- 在LSTM单元中引入随机潜变量，建模复杂时序动态和不确定性
- 无需改动原架构，通过非自回归生成实现强预测性能
- 在多个真实数据集上超越现有方法，适合高不确定性场景
扩展型长短期记忆网络（xLSTM）在建模时间序列的长期依赖关系方面表现优异。然而，其确定性结构限制了表达能力与预测性能，尤其在具有内在不确定性、随机性和复杂层级潜变量动态的真实世界数据上表现不足。本文提出StoxLSTM，一种基于设计的状态空间建模框架的随机xLSTM，将潜随机变量直接嵌入循环单元，有效建模深层潜时序动态与不确定性。该状态空间模型采用高效的非自回归生成方式，在不修改原始xLSTM架构的前提下，实现强大的预测性能。在多个公开基准数据集上的大量实验表明，StoxLSTM持续优于当前最优基线，具备更优性能与泛化能力。
原文摘要 · Abstract (English)
The Extended Long Short-Term Memory (xLSTM) network has demonstrated strong capability in modeling complex long-term dependencies in time series data. Despite its success, the deterministic architecture of xLSTM limits its representational capacity and forecasting performance, especially on challenging real-world time series datasets characterized by inherent uncertainty, stochasticity, and complex hierarchical latent dynamics. In this work, we propose StoxLSTM, a stochastic xLSTM within a designed state space modeling framework, which integrates latent stochastic variables directly into the recurrent units to effectively model deep latent temporal dynamics and uncertainty. The designed state space model follows an efficient non-autoregressive generative approach, achieving strong predictive performance without complex modifications to the original xLSTM architecture. Extensive experiments on publicly available benchmark datasets demonstrate that StoxLSTM consistently outperforms state-of-the-art baselines, achieving superior performance and generalization.
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