Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE. Specifically, a coupled diffusion probabilistic model is proposed to augment the time series data without increasing the aleatoric uncertainty and implement a more tractable inference process with BVAE. To ensure the generated series move toward the true target, we further propose to adapt and integrate the multiscale denoising score matching into the diffusion process for time series forecasting. In addition, to enhance the interpretability and stability of the prediction, we treat the latent variable in a multivariate manner and disentangle them on top of minimizing total correlation. Extensive experiments on synthetic and real-world data show that D3VAE outperforms competitive algorithms with remarkable margins. Our implementation is available at https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE.
Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field. Though deep learning methods have achieved promising results, there are still many limitations, for example, how to extract clean features from the raw stock data. In this paper, we introduce an \emph{Augmented Disentanglement Distillation (ADD)} approach to remove interferential features from the noised raw data. Specifically, we present 1) a disentanglement structure to separate excess and market information from the stock data to avoid the two factors disturbing each ot…
This paper propose DualVDT, a generative model for Time-series forecasting. Introduced dual reparametrized variational mechanisms on variational autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model, prove the advance performance analytically. This mechanism leverage the latent score based generative model (SGM), explicitly denoising the perturbation accumulated on latent vector through reverse time stochastic differential equation and variational ancestral sampling. The posterior of denoised latent distribution fused with dual reparametrized variational density. The KL div…
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Peng Cheng Laboratory（鹏城实验室）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shantou University（汕头大学）
The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Average. However, by utilizing the Wavelet Transform in place of the Moving Average to denoise stock signals, financial data can be smoothened and more accurately broken down. This newly transformed, denoised, and more stable stock data can be …
Frequency-Aware Attention-LSTM for PM$_{2.5}$ Time Series Forecasting
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；College of Computer Science and AI Southwest Minzu University（西南民族大学计算机科学与人工智能学院）；School of Information Technology Guangxi Police College（广西警察学院信息技术学院）
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
College of Computer Science, Sichuan University, Chengdu, China（四川大学计算机学院）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（中国科学院计算技术研究所）；Institute of Artificial Intelligence, University of Central Florida, Orlando, USA（佛罗里达大学人工智能研究所）
Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards
Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
East China Normal University（东华大学）；Nanyang Technological University（南洋理工大学）；Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China（电子科技大学）
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；College of Computer Science, Xiangtan University（湘潭大学计算机学院）；Tencent AI Lab（腾讯AI实验室）；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机系）
Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting
Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；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）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；College of Computer Science, Chongqing University（重庆大学计算机学院）
Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block…
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Jiaotong University（北京交通大学）；University of Science and Technology of China（中国科学技术大学）
HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
Lawrence Berkeley National Laboratory（伯克利劳伦斯国家实验室）；University of Oklahoma（俄克拉荷马大学）；Cornell University（康奈尔大学）；International Computer Science Institute（国际计算机科学研究所）
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Revisiting PCA for time series reduction in temporal dimension
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）；Eastern Institute of Technology（东方理工学院）；Hefei University of Technology（合肥工业大学）
STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
Machine Learning for Physical Sciences (ML4Sci/e) Group, Department of Mathematics \& Computer Science, Eindhoven University of Technology, The Netherlands
