xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories

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. Our extensive evaluations demonstrate its superior long-term forecasting performance compared to recent state-of-the-art methods. 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 time series forecasting. Time series are an essential data modality ubiquitous in many critical fields of application, such as medicine (Hosseini et al., 2021), manufacturing (Essien & Giannetti, 2020), logistics (Seyedan & Mafakheri, 2020), traffic management (Lippi et al., 2013), finance (Lin et al., 2012), audio processing (Latif et al., 2023), and weather modeling (Lam et al., 2023).