The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
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）；Technical University of Munich（慕尼黑工业大学）；Warsaw University of Technology（华沙工业大学）；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）
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker, Ernest Pokropek, Willa Potosnak, Suyash Bire, Salomey Osei, Björn Lütjens
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. In comparison, graph neural networks (GNNs) are capable of modeling large-scale spatial dependencies and are more interpretable due to the explicit modeling of information flow through edge connections. We propose the first application of graph neural networks to seasonal forecasting. We design a novel graph connectivity learning module that enables our GNN model to learn large-scale spatial interactions jointly with the actual ENSO forecasting task. Our model, \graphino, outperforms state-of-the-art deep learning-based models for forecasts up to six months ahead. Additionally, we show that our model is more interpretable as it learns sensible connectivity structures that correlate with the ENSO anomaly pattern.
The University of Georgia（佐治亚大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
In this work, we study the pandemic course in the United States by considering national and state levels data. We propose and compare multiple time-series prediction techniques which incorporate auxiliary variables. One type of approach is based on spatio-temporal graph neural networks which forecast the pandemic course by utilizing a hybrid deep learning architecture and human mobility data. Nodes in this graph represent the state-level deaths due to COVID-19, edges represent the human mobility trend and temporal edges correspond to node attributes across time. The second approach is based on…
Middle East Technical University（中东技术大学）；Massachusetts Institute of Technology（麻省理工学院）
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid me…
University of Washington（华盛顿大学）；Microsoft Research（微软研究院）
We present a significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an offline volume-conservative mapping to a cubed-sphere grid, improvements to the CNN architecture, and the minimization of the loss function over multiple steps in a prediction sequence. The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding …
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, …
National University of Singapore（新加坡国立大学）；Monash University（莫纳什大学）
The abrupt outbreak of the COVID-19 pandemic was the most significant event in 2020, which had profound and lasting impacts across the world. Studies on energy markets observed a decline in energy demand and changes in energy consumption behaviors during COVID-19. However, as an essential part of system operation, how the load forecasting performs amid COVID-19 is not well understood. This paper aims to bridge the research gap by systematically evaluating models and features that can be used to improve the load forecasting performance amid COVID-19. Using real-world data from the New York Inde…
Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Cornell University, Department of Statistics and Data Science（康奈尔大学统计与数据科学系）；Washington University in St. Louis, Department of Statistics and Data Science（圣路易斯华盛顿大学统计与数据科学系）；Cornell University, Department of Earth and Atmospheric Sciences（康奈尔大学地球与大气科学系）
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
Nanjing University of Information Science and Technology（南京信息工程大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hongkong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Lucas English School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Lucas English 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）；Mahdi Abolghasemi School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Mahdi Abolghasemi 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）
This paper improves wind power prediction via weather forecast-contextualized Long Short-Term Memory Neural Network (LSTM) models. Initially, only wind power data was fed to a generic LSTM, but this model performed poorly, with erratic and naive behavior observed on even low-variance data sections. To address this issue, weather forecast data was added to better contextualize the power data, and LSTM modifications were made to address specific model shortcomings. These models were tested through both a Normalized Mean Absolute Error and the Naive Ratio (NR), which is a score introduced by this…
University of Minnesota（明尼苏达大学）；Shanghai Jiao Tong University（上海交通大学）；University of California, Irvine（加州大学欧文分校）
Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making. Improvement of the forecasting accuracy, even by a small percentage, may have a substantial impact on companies' production and financial planning, marketing strategies, inventory controls, supply chain management, and eventually stock prices. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motiv…
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting
Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Aalborg University（奥尔堡大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Improving accuracy in short mortality rate series: Exploring Multi-step Forecasting Approaches in Hybrid Systems
Departamento de Finanças e Contabilidade Universidade Federal da Paraíba（财务与会计系 伯南布哥联邦大学）；Centro de Informática Universidade Federal de Pernambuco（计算机中心 皮奥伊联邦大学）；Departamento de Estatística e Informática Universidade Federal do Cariri（统计与计算机系 卡里里联邦大学）
EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting
Biocomplexity Institute, University of Virginia（弗吉尼亚大学生物复杂性研究所）；Virginia Department of Health（弗吉尼亚州卫生部门）；US Department of Defense, Pentagon（美国国防部五角大楼）；Old Dominion University（奥多明尼昂大学）
Department of Earth and Atmospheric Sciences, University of Houston（休斯顿大学地球与大气科学系）；Houston Advanced Research Center（休斯顿高级研究中心）
Advancements in numerical weather prediction models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization and linearization of the differential equations that reduce forecasting accuracy. In this work, we investigate the use of a computationally efficient deep learning method, the Convolutional Neural Network (CNN), as a post-processing technique that improves mesoscale Weather and Research Forecasting (WR…
Tsinghua University（清华大学）；University of California, Berkeley（加州大学伯克利分校）
In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. However, incorporating ML is generally considered to sacrifice interpretability inevitably. In addition, existing hybrid algorithms usually rely on theoretical models with statistical assumptions and focus only on the accuracy of aggregate predictions, and thus suffer from accuracy …
Improving Sequential Market Coordination via Value-oriented Renewable Energy Forecasting
College of Engineering, Cornell University（康奈尔大学工程学院）；Department of Electrical and Computer Engineering, University of California, San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电气工程系）；Dyson School of Design Engineering, Imperial College London（伦敦帝国理工学院设计工程学院）
Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model
a LMD/IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS,Université PSL, Sorbonne Université, CNRS, Palaiseau, 91120, France b INRIA, Paris, France c EDF R\&D, Palaiseau, France
Central South University（中南大学）；Caledonian University（喀里多尼亚大学）
As an important part of the power system, power load forecasting directly affects the national economy. The data shows that improving the load forecasting accuracy by 0.01% can save millions of dollars for the power industry. Therefore, improving the accuracy of power load forecasting has always been the pursuing goals for a power system. Based on this goal, this paper proposes a novel connection, the dense average connection, in which the outputs of all preceding layers are averaged as the input of the next layer in a feed-forward fashion. Based on dense average connection , we construct the …
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…
Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Center for Ocean Mega-Science, Chinese Academy of Sciences（中国科学院海洋大科学研究中心）
AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
Wageningen University and Research（瓦赫宁根大学与研究中心）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space
Wuhan University（武汉大学）；Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere（湖北省陆气定量遥感重点实验室）；School of Remote Sensing and Information Engineering（遥感信息工程学院）；Hubei Luojia Laboratory（湖北珞珈实验室）
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are gaining popularity. One of the most important categories of data-driven approaches relies on a predefined or learned health indicator to characterize the equipment condition up to the present time and make inference on how it is likely to evolve in the future. In these approaches, health indicator forecasting that constructs the health indicator cur…
Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
Expertise Center for Climate Extremes (ECCE), University of Lausanne（气候极端专家中心（ECCE）、洛桑大学）；École polytechnique, Institut Polytechnique de Paris（巴黎高等理工学院）；Institute of Geography - Oeschger Centre for Climate Change Research, University of Bern（地理研究所—奥施格气候变迁研究中心、伯恩大学）；Federal Office of Meteorology and Climatology MeteoSwiss（联邦气象与气候办公室梅托斯瓦尔）；Faculty of Geosciences and Environment, University of Lausanne（地球科学与环境学院、洛桑大学）
University of Georgia（佐治亚大学）；Kennesaw State University（肯尼索州立大学）
Statistical methods such as the Box-Jenkins method for time-series forecasting have been prominent since their development in 1970. Many researchers rely on such models as they can be efficiently estimated and also provide interpretability. However, advances in machine learning research indicate that neural networks can be powerful data modeling techniques, as they can give higher accuracy for a plethora of learning problems and datasets. In the past, they have been tried on time-series forecasting as well, but their overall results have not been significantly better than the statistical model…
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, Emory University（转化神经影像与数据科学三机构中心（TReNDS）：佐治亚州立大学、佐治亚理工学院、埃默里大学）
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies). Collaborating with domain experts, we design a visual analytics system, AttnAnalyzer, that enables users to explo…
UC Berkeley（加州大学伯克利分校）；MIT（麻省理工学院）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；University of Oxford（牛津大学）
Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organ…
Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Université du Québec à Rimouski（魁北克大学里穆斯基分校）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）
What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
School of Information Science and Technology, ShanghaiTech University, Shanghai, China（信息科学与技术学院，上海科技大学，上海，中国）；University of Illinois at Urbana-Champaign, Illinois, United States（伊利诺伊大学厄巴纳-香槟分校，伊利诺伊，美国）
Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks' performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpf…
