The recently proposed xLSTM is a powerful model that leverages expressive multiplicative gating and residual connections, providing the temporal capacity needed for long-horizon forecasting and representation learning. This architecture has demonstrated success in time series forecasting, lossless compression, and even large-scale language modeling tasks, where its linear memory footprint and fast inference make it a viable alternative to Transformers. Despite its growing popularity, no prior work has explored xLSTM for anomaly detection. In this work, we fill this gap by proposing xLSTMAD, the first anomaly detection method that integrates a full encoder-decoder xLSTM architecture, purpose-built for multivariate time series data. Our encoder processes input sequences to capture historical context, while the decoder is devised in two separate variants of the method. In the forecasting approach, the decoder iteratively generates forecasted future values xLSTMAD-F, while the reconstruction approach reconstructs the input time series from its encoded counterpart xLSTMAD-R. We investigate the performance of two loss functions: Mean Squared Error (MSE), and Soft Dynamic Time Warping (SoftDTW) to consider local reconstruction fidelity and global sequence alignment, respectively. We evaluate our method on the comprehensive TSB-AD-M benchmark, which spans 17 real-world datasets, using state-of-the-art challenging metrics such as VUS-PR. In our results, xLSTM showcases state-of-the-art accuracy, outperforming 23 popular anomaly detection baselines. Our paper is the first work revealing the powerful modeling capabilities of xLSTM for anomaly detection, paving the way for exciting new developments on this subject. Our code is available at: https://github.com/Nyderx/xlstmad.

Efficient flight scheduling is crucial to properly allocate airline resources, but even the best flight schedule has to face unexpected delays and disruptions. The ability to recover from such disruptions is essential for airlines to minimize the negative impact on their revenue and reputation. In this context, machine learning-based methods can be used to identify suitable recovery methods as unexpected events occur. Reinforcement learning approaches are especially promising since they extract suitable solutions much more efficiently than conventional optimization and meta-heuristics methods and provide timely rescheduling capabilities for airlines, which translates into reduced capital and reputation losses. However, current works either do not leverage deep learning or focus on simple scenarios that do not fully entail real-world complexities, resulting in limited efficiency or sub-optimal solutions. In this paper, we propose an ensemble of two deep learning approaches: Deep Double Q-Learning (DDQL) and Advantage Actor-Critic (A2C). The models aim to minimize the total delays caused by disruptions by swapping aircraft and delaying flights as recovery options. We perform experiments with a benchmark dataset and a real-world airline dataset, showing that our method is effective in providing a significant reduction of delays caused by disruptions.