StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting
This work addresses forecasting challenges in time series data for applications in multiple research communities, but it is incremental as it builds on the existing xLSTM architecture.
The authors tackled the problem of improving time series forecasting by proposing StoxLSTM, a stochastic extension of the xLSTM network, which consistently outperformed state-of-the-art baselines on benchmark datasets with better robustness and generalization.
The Extended Long Short-Term Memory (xLSTM) network has attracted widespread research interest due to its enhanced capability to model complex temporal dependencies in diverse time series applications. Despite its success, there is still potential to further improve its representational capacity and forecasting performance, particularly on challenging real-world datasets with unknown, intricate, and hierarchical dynamics. In this work, we propose a stochastic xLSTM, termed StoxLSTM, that improves the original architecture into a state space modeling framework by incorporating stochastic latent variables within xLSTM. StoxLSTM models the latent dynamic evolution through specially designed recurrent blocks, enabling it to effectively capture the underlying temporal patterns and dependencies. Extensive experiments on publicly available benchmark datasets from multiple research communities demonstrate that StoxLSTM consistently outperforms state-of-the-art baselines with better robustness and stronger generalization ability.