The supply and demand of energy is influenced by meteorological conditions. The relevance of accurate weather forecasts increases as the demand for renewable energy sources increases. The energy providers and policy makers require weather information to make informed choices and establish optimal plans according to the operational objectives. Due to the recent development of deep learning techniques applied to satellite imagery, weather forecasting that uses remote sensing data has also been the subject of major progress. The present paper investigates multiple steps ahead frame prediction for coastal sea elements in the Netherlands using U-Net based architectures. Hourly data from the Copernicus observation programme spanned over a period of 2 years has been used to train the models and make the forecasting, including seasonal predictions. We propose a variation of the U-Net architecture and further extend this novel model using residual connections, parallel convolutions and asymmetric convolutions in order to introduce three additional architectures. In particular, we show that the architecture equipped with parallel and asymmetric convolutions as well as skip connections outperforms the other three discussed models.
Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting
Oden Institute for Computational Engineering & Sciences University of Texas at Austin（奥登计算工程与科学研究所 乌得勒支大学）；Department of Civil & Environmental Engineering & Earth Sciences University of Notre Dame（土木与环境工程及地球科学系 纽约大学）；Department of Data Science Norwegian University of Life Sciences（数据科学系 挪威生命科学大学）；Department of Numerical Analysis and Scientific Computing Simula Research Laboratory（数值分析与科学计算系 Simula 研究实验室）
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables f…
Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Northwest Polytechnical University（西北工业大学）；Zhejiang University（浙江大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）
Atos（源讯）；Flanders Research Institute for Agriculture, Fisheries and Food（法兰德斯农业、渔业和食品研究所）
This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea. By combining fisheries related features with environmental data, sea bottom temperature derived from remote sensing, a higher accuracy can be achieved. In a forecast setting, the predictive accuracy is further improved by predicting, using a recurrent deep neural network, the sea bottom temperature up to four days in advance instead of relying on the last p…
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural model, in comparison to the standard method, by generating network parameters in accordance with the data and target function features. A pattern-based representation of time series makes the proposed approach useful for forecasting time series with multiple seasonality. In the simulation study, we evaluate the performance of the proposed models and find that they can compete in terms of forecasting accuracy with fully-…
An emerging number of modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. Specifically, time series with multiple seasonality is a difficult task with comparatively fewer discussions. In this paper, we propose a two-stage method for time series with multiple seasonality, which does not require pre-determined seasonality periods. In the first stage, we generalize the classical seasonal autoregressive moving average (ARMA) model in multiple seasonality regime. In the second stage, we utilize an appropriate criterion for lag or…
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…
Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
Centro de Tecnologías de la Imagen (CTIM)（图像技术中心）；Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES)（网络、企业与社会大学研究所）；University of Las Palmas de Gran Canaria, Spain（大加那利岛大学）；Oceanografía Física y Geofísica Aplicada (OFYGA)（应用物理海洋学与地球物理研究所）；Instituto Universitario de Investigación en Acuicultura Sostenible y Ecosistemas Marinos (ECOAQUA)（可持续水产养殖与海洋生态系统大学研究所）
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
School of Transportation Engineering, East China Jiao Tong University（东华交通大学交通运输工程学院）；Jiangxi Provincial Key Laboratory of Water Ecological Conservation in Headwater Regions（江西省头水地区水生态保育重点实验室）；Division of Geodetic Science, School of Earth Sciences, The Ohio State University（地球科学学院地质科学系，俄亥俄州立大学）；Institute Dom Luiz, University of Beira Interior（贝拉内里亚大学多姆路易斯研究所）；Institute of Meteorology and Climatology, Leibniz University Hannover（汉诺威莱比锡大学气象与气候研究所）；Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University（江西省鄱阳湖湿地与流域研究重点实验室，江西省教育厅，江西师范大学）
Deep learning (DL) in general and Recurrent neural networks (RNNs) in particular have seen high success levels in sequence based applications. This paper pertains to RNNs for time series modelling and forecasting. We propose a novel RNN architecture capturing (stochastic) seasonal correlations intelligently while capable of accurate multi-step forecasting. It is motivated from the well-known encoder-decoder (ED) architecture and multiplicative seasonal auto-regressive model. It incorporates multi-step (multi-target) learning even in the presence (or absence) of exogenous inputs. It can be empl…
Chiang Mai University（清迈大学）；Data Science Research Center（数据科学研究中心）
Short-term precipitation forecasting is essential for planning of human activities in multiple scales, ranging from individuals' planning, urban management to flood prevention. Yet the short-term atmospheric dynamics are highly nonlinear that it cannot be easily captured with classical time series models. On the other hand, deep learning models are good at learning nonlinear interactions, but they are not designed to deal with the seasonality in time series. In this study, we aim to develop a forecasting model that can both handle the nonlinearities and detect the seasonality hidden within the…
Seoul National University（首尔大学）；Korea Aerospace University（韩国航空大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns. We propose a mixed hierarchical seasonality (MHS) model. Intermediate parameters for each seasonal period are first estimated, and a mixture of intermediate parameters is taken. This results in a model that automatically le…
Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approach…
MIT Operations Research Center（麻省理工学院运筹学研究中心）；MIT Laboratory for Financial Engineering（麻省理工学院金融工程实验室）；Machine Intelligence Technology, Alibaba Group（阿里巴巴集团机器智能技术部）
Seasonal time series Forecasting remains a challenging problem due to the long-term dependency from seasonality. In this paper, we propose a two-stage framework to forecast univariate seasonal time series. The first stage explicitly learns the long-range time series structure in a time window beyond the forecast horizon. By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon. In both stages, we integrate the auto-regressive model with neural networks to capture both linear and non-linear characteristics in time series. Ou…
Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting
Department of Environmental Sciences, Louisiana State University（环境科学系，路易斯安那州立大学）；Center for Computation and Technology, Louisiana State University（计算与技术中心，路易斯安那州立大学）；Department of Computer Science, Louisiana State University（计算机科学系，路易斯安那州立大学）；Department of Computer Science, Furman University（计算机科学系，富兰克林大学）；Department of Oceanography and Coastal Sciences, Louisiana State University（海洋学与沿海科学系，路易斯安那州立大学）
Hohai University（河海大学）；California State University, Fullerton（加州州立大学富勒顿分校）
Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empirical-based or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both short-term and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) app…
