Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existing works usually learn spatial and temporal dependencies separately, ignoring the dependencies crossing spatial and temporal dimensions. In this paper, we propose DSTCGCN, a dynamic spatial-temporal cross graph convolution network to learn dynamic spatial and temporal dependencies jointly via graphs for traffic forecasting. Specifically, we introduce a fast Fourier transform (FFT) based attentive selector to choose relevant time steps for each time step based on time-varying traffic data. Given the selected time steps, we introduce a dynamic cross graph construction module, consisting of the spatial graph construction, temporal connection graph construction, and fusion modules, to learn dynamic spatial-temporal cross dependencies without pre-defined priors. Extensive experiments on six real-world datasets demonstrate that DSTCGCN achieves the state-of-the-art performance.
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Huawei Technologies Ltd.（华为技术有限公司）
Multivariate time series forecasting has been widely used in various practical scenarios. Recently, Transformer-based models have shown significant potential in forecasting tasks due to the capture of long-range dependencies. However, recent studies in the vision and NLP fields show that the role of attention modules is not clear, which can be replaced by other token aggregation operations. This paper investigates the contributions and deficiencies of attention mechanisms on the performance of time series forecasting. Specifically, we find that (1) attention is not necessary for capturing temp…
Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting
Northwestern Polytechnical University（西北工业大学）；City University of Hong Kong（香港城市大学）；Shenzhen Research Institute, City University of Hong Kong（香港城市大学深圳研究院）；Northwest University（西北大学）
APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting
Hohai University（河海大学）；Key Laboratory of Water Big Data Technology of Ministry of Water Resources（水利部水大数据技术重点实验室）；College of Computer and Software, Hohai University（河海大学计算机与软件学院）
Multivariate time series (MTS) forecasting has attracted much attention in many intelligent applications. It is not a trivial task, as we need to consider both intra-variable dependencies and inter-variable dependencies. However, existing works are designed for specific scenarios, and require much domain knowledge and expert efforts, which is difficult to transfer between different scenarios. In this paper, we propose a scale-aware neural architecture search framework for MTS forecasting (SNAS4MTF). A multi-scale decomposition module transforms raw time series into multi-scale sub-series, whic…
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京先进创新中心）；Beihang University（北京航空航天大学）；HKGAI（Hong Kong University of Science and Technology）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究 institute）；Southern University of Science and Technology（南方科技大学）；Department of Data Science（数据科学系）
Central University of Finance and Economics（中央财经大学）；Beijing Wuzi University（北京物资学院）
Analyzing both temporal and spatial patterns for an accurate forecasting model for financial time series forecasting is a challenge due to the complex nature of temporal-spatial dynamics: time series from different locations often have distinct patterns; and for the same time series, patterns may vary as time goes by. Inspired by the successful applications of deep learning, we propose a new model to resolve the issues of forecasting household leverage in China. Our solution consists of multiple RNN-based layers and an attention layer: each RNN-based layer automatically learns the temporal pat…
The accurate forecasting of infectious epidemic diseases is the key to effective control of the epidemic situation in a region. Most existing methods ignore potential dynamic dependencies between regions or the importance of temporal dependencies and inter-dependencies between regions for prediction. In this paper, we propose an Inter- and Intra-Series Embeddings Fusion Network (SEFNet) to improve epidemic prediction performance. SEFNet consists of two parallel modules, named Inter-Series Embedding Module and Intra-Series Embedding Module. In Inter-Series Embedding Module, a multi-scale unifie…
Geneva School of Business Administration, HES-SO University of Applied Sciences of Western Switzerland（日内瓦商学院，瑞士西部应用科学与艺术大学）；University of Geneva（日内瓦大学）
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed …
Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Academy of Cyber（网络研究院）
Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
National Key Laboratory for Novel Software Technology（现代软件工程国家重点实验室）；Nanjing University（南京大学）；School of Artificial Intelligence（人工智能学院）；School of Electronic Science and Engineering（电子科学与工程学院）
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Cambridge（剑桥大学）
Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting
Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；ShenZhen University（深圳大学）；Shenzhen Energy Group Co., Ltd.（深圳能源集团有限公司）；South China University of Technology（华南理工大学）
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）
Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics
University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Digital Financial Information Technology Co.LTD（数字金融信息技术有限公司）；Northern Arizona University（北亚利桑那大学）
Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is challenging due to the fact that the road network contains complex and time-varying spatial-temporal dependencies. Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies. However, the existing methods often construct the graph only based on road network …
DB2-TransF: All You Need Is Learnable Daubechies Wavelets for Time Series Forecasting
organization= Department of Computer Science \& Engineering \& Communication \& Engineering , addressline= G B Pant , city= New Delhi , postcode= 580007 , state= Delhi , country= India；organization= Department of Computer Science \& Engineering , addressline= Indian Institute of Technology , city= Dharwad , postcode= 580007 , state= Karnataka , country= India
