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Found 7 papers, 2 papers with code
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns.
TimeCMA: Towards LLM-Empowered Time Series Forecasting via Cross-Modality Alignment
Then, we design a cross-modality alignment module to retrieve high-quality and pure time series embeddings from the prompt embeddings.
LightTR: A Lightweight Framework for Federated Trajectory Recovery
Due to the limited acquisition capabilities of edge devices, a lot of trajectories are recorded at a low sampling rate, which may lead to the effectiveness drop of urban applications.
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e. g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is often essential to enable safety, predictability, or reliability.
Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
In this paper, we study the novel problem of multi-channel traffic flow prediction, and propose a deep \underline{M}ulti-\underline{V}iew \underline{C}hannel-wise \underline{S}patio-\underline{T}emporal \underline{Net}work (MVC-STNet) model to effectively address it.
VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System
We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems.
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.
