Publication
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
Maurice Kraus; Felix Divo; Devendra Singh Dhami; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2410.16928, Pages 1-16, arXiv, 2024.
Abstract
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 evalu-
ations demonstrate its superior long-term forecasting performance compared to
recent state-of-the-art methods. A thorough model analysis provides further in-
sights into its key components and confirms its robustness and effectiveness. This
work contributes to the resurgence of recurrent models in time series forecasting.