Lan Wu

AAAI Conference 2026 Conference Paper

ExoTimer: Leveraging Large Language Models for Time Series Forecasting with Exogenous Variables

• Lan Wu
• Xuebin Wang
• Chenglong Ge
• Ruijuan Chu
• LinYu Wang

Real-world systems often exhibit complex behaviors and are influenced by various external factors, making the integration of exogenous variables essential for accurate and robust time series forecasting. However, modeling time series with exogenous variables remains challenging due to dynamic cross-variable dependencies and the semantic gap between numerical time series data and external contextual knowledge. Large language models (LLMs) have demonstrated powerful language understanding and knowledge representation capabilities in real-world systems, offering a promising solution to bridge this gap. Motivated by this, we propose ExoTimer, a framework that deeply integrates LLMs for time series modeling with exogenous variables. We begin by introducing an Exo-Aware Endogenous Encoder to dynamically incorporate important exogenous variable information and generate patch-level representations for endogenous variables. To leverage the rich knowledge in LLMs, a Multi-Attribute Prompt Embedding module is elaborately designed to convert heterogeneous temporal features, contextual information and task specifications into LLM-interpretable textual prompts. Additionally, we propose Bi-Hash Alignment, a lightweight cross-modal alignment mechanism that bridges textual and temporal modalities in a shared hash space. Finally, a Dual-Branch Predictor with a learnable coefficient is employed to obtain the final time series prediction by integrating temporal-text and text-temporal representations. Extensive experiments on twelve real-world datasets demonstrate that ExoTimer achieves state-of-the-art performance and exhibits generalizability and scalability in both few-shot and zero-shot scenarios.

ICRA Conference 2025 Conference Paper

DynORecon: Dynamic Object Reconstruction for Navigation

• Yiduo Wang 0001
• Jesse Morris
• Lan Wu
• Teresa A. Vidal-Calleja
• Viorela Ila

This paper presents DynORecon, a Dynamic Object Reconstruction system that leverages the information provided by Dynamic SLAM to simultaneously generate a volumetric map of observed moving entities while estimating free space to support navigation. By capitalising on the motion estimations provided by Dynamic SLAM, DynORecon continuously refines the representation of dynamic objects to eliminate residual artefacts from past observations and incrementally reconstructs each object, seamlessly integrating new observations to capture previously unseen structures. Our system is highly efficient (~20 FPS) and produces accurate (~10 cm) object reconstructions using simulated and real-world outdoor datasets.

UAI Conference 2025 Conference Paper

FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction

• Lan Wu
• Xuebin Wang
• Ruijuan Chu
• Guangyi Liu
• Jing Zhang
• Linyu Wang

Accurate multi-step flight trajectory prediction plays an important role in Air Traffic Control, which can ensure the safety of air transportation. Two main issues limit the flight trajectory prediction performance of existing works. The first issue is the negative impact on prediction accuracy caused by the significant differences in data range. The second issue is that real-world flight trajectories involve underlying temporal dependencies, and most existing methods fail to reveal the hidden complex temporal variations and extract features from one single time scale. To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction. Specifically, FlightPatchNet first utilizes differential coding to encode the original values of longitude and latitude into first-order differences and generates embeddings for all variables at each time step. Then, global temporal attention is introduced to explore the dependencies between different time steps. To fully explore the diverse temporal patterns in flight trajectories, a multi-scale patch network is delicately designed to serve as the backbone. The multi-scale patch network exploits stacked patch mixer blocks to capture inter- and intra-patch dependencies under different time scales, and further integrates multi-scale temporal features across different scales and variables. Finally, FlightPatchNet ensembles multiple predictors to make direct multi-step prediction. Extensive experiments on ADS-B datasets demonstrate that our model outperforms the competitive baselines.

EAAI Journal 2025 Journal Article

State slow feature softmax Q-value regularization for offline reinforcement learning

• Lan Wu
• Quan Liu
• Renyang You

IROS Conference 2023 Conference Paper

Pseudo Inputs Optimisation for Efficient Gaussian Process Distance Fields

• Lan Wu
• Cedric Le Gentil
• Teresa A. Vidal-Calleja

Robots reason about the environment through dedicated representations. Despite the fact that Gaussian Process (GP)-based representations are appealing due to their probabilistic and continuous nature, the cubic computational complexity is a concern. In this paper, we present a novel efficient GP-based representation that has the ability to produce accurate distance fields and is parameterised by the optimal locations of pseudo inputs. When applying the proposed method together with a kernel approximation approach, we show it outperforms well-established sparse GP frameworks in efficiency and accuracy. Moreover, we extend the proposed method to work in a dynamic setting, where a map is built iteratively and the scene dynamics are accounted for by adding or removing objects from the environment representation. In a nutshell, our method provides the ability to infer dynamic distance fields and achieve state-of-the-art reconstruction efficiently.