Chenxi Sun

TL;DR: This work aims to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM, named TEST, and shows that the pre-trained LLM with TEST strategy can achieve better or comparable performance than today's SOTA TS models and offer benefits for few-shot and generalization.

TL;DR: A novel iterative clustering method for classifying time series of EV charging rates based on their “tail features”, which applies to ACN-Data, a fine-grained EV charging dataset recently made publicly available, to illustrate its effectiveness and potential applications.

TL;DR: This paper proposes three citizen-centric objectives for the optimal sensor placement problem, which does not require the prior deployment of pollution sensors for obtaining any field information, and formulate the optimization problem for each scenario and propose an effective method to solve the problem accordingly.

TL;DR: A Hypergraph Structure Learning (HSL) framework is proposed, which optimizes the hypergraph structure and the HGNNs simultaneously in an end-to-end way and outperforms the state-of-the-art baselines while adaptively sparsifying hypergraph structures.