Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and complex spatial-temporal dependency of road networks. For this task, we propose Graph Attention-Convolution-Attention Networks (GACAN). The model uses a novel Att-Conv-Att (ACA) block which contains two graph attention layers and one spectral-based GCN layer sandwiched in between. The graph attention layers are meant to capture temporal features while the spectral-based GCN layer is meant to capture spatial features. The main novelty of the model is the integration of time series of four different time granularities: the original time series, together with hourly, daily, and weekly time series. Unlike previous work that used multi-granularity time series by handling every time series separately, GACAN combines the outcome of processing all time series after each graph attention layer. Thus, the effects of different time granularities are integrated throughout the model. We perform a series of experiments on three real-world datasets. The experimental results verify the advantage of using multi-granularity time series and that the proposed GACAN model outperforms the state-of-the-art baselines.
UC Santa Barbara（加州大学圣巴巴拉分校）；Brown University（布朗大学）；UC Los Angeles（加州大学洛杉矶分校）；Columbia University（哥伦比亚大学）
Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers that prepares the training windows through random sampling. As input time steps need not be consecutive, the number of distinct samples increases from linearly to combinatorially many. By breaking the temporal order, this technique also helps transformers to capture dependencies am…
Rapidly growing product lines and services require a finer-granularity forecast that considers geographic locales. However the open question remains, how to assess the quality of a spatio-temporal forecast? In this manuscript we introduce a metric to evaluate spatio-temporal forecasts. This metric is based on an Opti- mal Transport (OT) problem. The metric we propose is a constrained OT objec- tive function using the Gini impurity function as a regularizer. We demonstrate through computer experiments both the qualitative and the quantitative charac- teristics of the Gini regularized OT problem…
University of California Santa Barbara（加州大学圣巴巴拉分校）
Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer [1]. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot-product self-attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canon…
DAMO Academy, Alibaba Group（阿里巴巴集团 DAMO 院）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-鲁克蒙特研究所）；Rajiv Gandhi University（拉朱·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒研究实验室）
Electric load forecasting is pivotal for power system operation, planning and decision-making. The rise of smart grids and meters has provided more detailed and high-quality load data at multiple levels of granularity, from home to bus and cities. Motivated by similar patterns of loads across different cities in a province in eastern China, in this paper we focus on the Multi-Region Electric Load Forecasting (MRELF) problem, targeting accurate short-term load forecasting for multiple sub-regions within a large region. We identify three challenges for MRELF, including regional variation, contex…
Technical University of Darmstadt（达姆施塔特工业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of São Paulo（圣保罗大学）；TU Munich（慕尼黑工业大学）；Warsaw University of Technology（华沙理工大学）；Duquesne University（杜肯大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Massachusetts Institute of Technology（麻省理工学院）
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns called teleconnections. Hence, we propose the application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods. The explicit modeling of information flow v…
For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experimenting with techniques from Natural Language Processing (Encoder-Decoder LSTMs) and Computer Vision (Dilated CNNs), as well as incorporating transfer learning. A novel contribution of this paper is the application of curriculum learning to neural network models built for time se…
FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts
Yunnan University（云南大学）；Carnegie Mellon University（卡内基梅隆大学）；Griffith University（格里菲斯大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Beijing Jiaotong University（北京交通大学）
Time series forecasting is an active research topic in academia as well as industry. Although we see an increasing amount of adoptions of machine learning methods in solving some of those forecasting challenges, statistical methods remain powerful while dealing with low granularity data. This paper introduces a refined Bayesian exponential smoothing model with the help of probabilistic programming languages including Stan. Our model refinements include additional global trend, transformation for multiplicative form, noise distribution and choice of priors. A benchmark study is conducted on a r…
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Software, Shandong University（山东大学软件学院）；Nanyang Technological University（南洋理工大学）；Digital Research Institute of ENN Group（ENN集团数字研究院）
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
School of Computing and Information Technology（计算与信息科技学院）
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
University of Science and Technology of China（中国科学技术大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Baidu Inc.（百度公司）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI 分支）
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Zhili College, Tsinghua University（清华大学致理书院）；University of Copenhagen（哥本哈根大学）；University of Washington（华盛顿大学）
Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular temporal data, little attention is given to segmenting the time series and finding homogeneous patterns. In this paper, it is proposed to estimate behavioral profiles of individuals' activities over time using Gaussian Process-based models. In particular, the aim is to investigate …
Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model
Atos（源讯公司）；EDF（法国电力公司）；Laboratoire des Sciences du Climat & de l’Environnement（气候与环境科学实验室）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）；UVSQ（凡尔赛大学）；Université Paris-Saclay（巴黎萨克雷大学）；Laboratoire de Mathématiques d’Orsay（奥赛数学实验室）
Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
Czech Technical University in Prague（布拉格捷克技术大学）；Faculty of Information Technology（信息技术学院）；Brno University of Technology（布尔诺理工大学）；CESNET, a.l.e.（CESNET公司）
The Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织）
In this paper, the process of forecasting household energy consumption is studied within the framework of the nonparametric Gaussian Process (GP), using multiple short time series data. As we begin to use smart meter data to paint a clearer picture of residential electricity use, it becomes increasingly apparent that we must also construct a detailed picture and understanding of consumer's complex relationship with gas consumption. Both electricity and gas consumption patterns are highly dependent on various factors, and the intricate interplay of these factors is sophisticated. Moreover, sinc…
This work provides two statistical Gaussian forecasting methods for predicting First Daily Departure Times (FDDTs) of everyday use electric vehicles. This is important in smart grid applications to understand disconnection times of such mobile storage units, for instance to forecast storage of non dispatchable loads (e.g. wind and solar power). We provide a review of the relevant state-of-the-art driving behavior features towards FDDT prediction, to then propose an approximated Gaussian method which qualitatively forecasts how many vehicles will depart within a given time frame, by assuming th…
The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, chaotic systems can be repeatedly measured to produce arbitrarily-detailed information about the underlying attractor. Chaotic systems thus pose a unique challenge to modern statistical learning techniques, while retaining quantifiable mathematical properties that make them controllable and interpretable as benchmarks. Here, we present a growing database currently comprising 131 known chaotic dynamical systems spanning fields such as astrophysics, climatology, and bio…
Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting
School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Innovation Engineering College, Macau University of Science and Technology（澳门科技大学创新工程学院）
Leveraging graph neural networks and mobility data for COVID-19 forecasting
Postgraduate Program in Computer Science, Federal University of Ouro Preto（奥鲁普托联邦大学计算机科学研究生院）；Department of Computing, Federal University of Ouro Preto（奥鲁普托联邦大学计算机系）；National Center for Monitoring and Early Warning of Natural Disasters（国家自然灾害监测与预警中心）
Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect clouds forecasts can lead to major uncertainty in the overall accuracy of weather forecasts due to their intrinsic role in the Earth's climate system. Few studies have tackled this challenging problem from a machine learning point-of-view due to a shortage of high-resolution datasets with many historical observations globally. In this paper, we present a novel satellite-based dataset called ``CloudCast''. It consists of 70,080 images with 10 different cloud types for multip…
FusAD: Time-Frequency Fusion with Adaptive Denoising for General Time Series Analysis
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院，光学电子研究所（iOPEN），西北工业大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究所（TeleAI），中国电信）；Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）
A Dataset of Dengue Hospitalizations in Brazil (1999 to 2021) with Weekly Disaggregation from Monthly Counts
Dept. of Control and Automation Engineering, UNESP（控制与自动化工程系，UNESP）；Dept. of Environmental Science, UNESP（环境科学系，UNESP）；HairU Serviços e Tecnologia LTDA（HairU服务与技术有限公司）；recod.ai, UNICAMP（recod.ai，UNICAMP）
T-Rep: Representation Learning for Time Series using Time-Embeddings
Let it Care；PariSanté Campus（巴黎健康校园）；Université Paris Cité（巴黎西岱大学）；INRIA（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；Centre de Recherche des Cordeliers（科德利埃研究所）
PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series
Department of Computer Science（计算机科学系）；Indiana University（印第安纳大学）；Department of Biomedical Engineering（生物医学工程系）；Oregon Health & Science University（俄勒冈健康与科学大学）
Shandong University（山东大学）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-南洋理工大学联合人工智能研究中心（C-FAIR））；Lund University（隆德大学）；Nanyang Technological University（南洋理工大学）
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical …
