xLSTM-Mixer：通过标量记忆混合实现多变量时间序列预测
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
AI总结：
xLSTM-Mixer通过融合多种视角和混合架构，实现了多变量时间序列预测的高精度与低内存消耗。
摘要：
时间序列数据在众多领域中普遍存在，需要开发出稳健且准确的预测模型。捕捉时间序列内部和跨变量之间的模式对于可靠预测至关重要。我们引入了xLSTM-Mixer模型，该模型旨在有效整合时间序列、联合时间变量信息和多种视角以实现稳健的预测。我们的方法首先使用一个跨变量共享的线性预测，然后通过xLSTM块进行优化。这些块是建模复杂动态时间序列数据的关键要素。xLSTM-Mixer最终融合两种不同的视角以生成最终预测。我们的广泛评估证明，该方法在长期预测性能上优于最近最先进的方法，同时仅需极少的内存。深入的模型分析提供了对其关键组件的进一步见解，并确认了其稳健性和有效性。这项工作通过将循环模型与混合架构相结合，首次推动了预测领域的循环模型复兴。
英文摘要：
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions. We introduce xLSTM-Mixer, a model designed to effectively integrate temporal sequences, joint time-variate information, and multiple perspectives for robust forecasting. Our approach begins with a linear forecast shared across variates, which is then refined by xLSTM blocks. They serve as key elements for modeling the complex dynamics of challenging time series data. xLSTM-Mixer ultimately reconciles two distinct views to produce the final forecast. Our extensive evaluations demonstrate its superior long-term forecasting performance compared to recent state-of-the-art methods while requiring very little memory. A thorough model analysis provides further insights into its key components and confirms its robustness and effectiveness. This work contributes to the resurgence of recurrent models in forecasting by combining them, for the first time, with mixing architectures.
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。
