This work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-quality predictive models becomes feasible under the TL paradigm, enabling the generation of new proper models with few data. In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting. Then, traditional batch learning is compared against TL based models using real traffic flow data, collected by deployed loops managed by the City Council of Madrid (Spain). In addition, we apply Online Learning (OL) techniques, where model receives an update after each prediction, in order to adapt to traffic flow trend changes and incrementally learn from new incoming traffic data. The obtained experimental results shed light on the advantages of transfer and online learning for traffic flow forecasting, and draw practical insights on their interplay with the amount of available training data at the location of interest.
Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature
Lattice Lab, Toyota Motor Corporation / Machine Learning Research Group, University of Oxford（丰田公司 lattice 实验室 / 机器学习研究组，牛津大学）；The University of Osaka / Lattice Lab, Toyota Motor Corporation / RIKEN AIP（大阪大学 / 丰田公司 lattice 实验室 / RIKEN AIP）；Machine Learning Research Group, University of Oxford（机器学习研究组，牛津大学）
Overcoming Data Limitations in Internet Traffic Forecasting: LSTM Models with Transfer Learning and Wavelet Augmentation
Department of Computer Science, University of Northern British Columbia（北坎伯兰大学计算机科学系）；Department of Computer Science, Western University（西部大学计算机科学系）；Juniper Networks
Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL techniques tailor-made for specific tasks such as image recognition, signal processing, or speech analysis are being introduced at a fast pace with many improvements. However, for the domain of forecasting, the current state in the ML community is perhaps where other domains such as Natural Language Processing and Computer Vision were at several years ago. The field of forecasting has mainly been fostered by statisticians/eco…
Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
DISMI, University of Modena and Reggio Emilia, Italy（DISMI，Modena和Reggio Emilia大学）；En\&Tech, University of Modena and Reggio Emilia, Italy（En\&Tech，Modena和Reggio Emilia大学）
The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adversarial attacks. Although most of the literature focuses on integrity-based attacks, this paper proposes availability-based adversarial attacks, which can be more easily implemented by attackers. For each forecast instance, the availability attack position is optimally solved by mixed-integer reformulation of the artificial neural network. To tackle this attack, an adversarial t…
National University of Sciences and Technology（国立科技大学）；Information Technology University (ITU)（信息技术大学（ITU））
Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dyna…
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considerin…
National Chin-Yi University of Technology（国立勤益科技大学）；University of Tartu（塔尔图大学）
Renewable Energies (RE) have gained more attention in recent years since they offer clean and sustainable energy. One of the major sustainable development goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean energy for everyone. Among the world's all renewable resources, solar energy is considered as the most abundant and can certainly fulfill the target of SDGs. Solar energy is converted into electrical energy through Photovoltaic (PV) panels with no greenhouse gas emissions. However, power generated by PV panels is highly dependent on solar radiation received at a …
Google Research（谷歌研究院）；Massachusetts Institute of Technology（麻省理工学院）
The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practitioners choosing between different modeling methodologies.
University of Macau（澳门大学）；Victoria University（维多利亚大学）
Although transfer learning is proven to be effective in computer vision and natural language processing applications, it is rarely investigated in forecasting financial time series. Majority of existing works on transfer learning are based on single-source transfer learning due to the availability of open-access large-scale datasets. However, in financial domain, the lengths of individual time series are relatively short and single-source transfer learning models are less effective. Therefore, in this paper, we investigate multi-source deep transfer learning for financial time series. We propo…
A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting
School of Computer, Electronics and Information, Guangxi University（广西大学计算机与电子信息学院）；School of Electrical Engineering, Guangxi University（广西大学电气工程学院）；School of Mathematics and Statistics, Xuzhou University of Technology（徐州工程学院数学与统计学院）
Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP) together with the availability of vast amounts of textual data in form of news articles, social media postings, etc., an increasing number of studies incorporate text-based predictors in forecasting models. We contribute to this literature by introducing weak learning, a recently proposed NLP approach to address the problem that text data is unlabeled. Without a dependent variable, it is not possible to finetune pr…
Forecasting of time series in continuous systems becomes an increasingly relevant task due to recent developments in IoT and 5G. The popular forecasting model ARIMA is applied to a large variety of applications for decades. An online variant of ARIMA applies the Online Newton Step in order to learn the underlying process of the time series. This optimization method has pitfalls concerning the computational complexity and convergence. Thus, this work focuses on the computational less expensive Online Gradient Descent optimization method, which became popular for learning of neural networks in r…
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches. In this work, we investigate neural networks (NN) as physically-sound data-driven representations of such systems. Reinterpreting Runge-Kutta methods as graphical models, we consider a residual NN architecture and introduce bilinear layers to embed non-linearities…
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional turbulence forecasting approaches heavily rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. The recent availability of high-resolution weather data and turbulence records allows more accurate forecasting of the turbulence in a data-driven way. However, it is a non-trivial task for developing a machine learning based turbulence forecasting system due …
A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only…
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Forecasting Science, Chinese Academy of Sciences（中国科学院预测科学研究中心）
The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting, but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four c…
Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling
State Key Laboratory of Severe Weather Meteorological Science and Technology, Nanjing University（南京大学灾害性天气气象科技国家重点实验室）；School of Atmospheric Science, Nanjing University（南京大学大气科学学院）；Ant Healthcare, Ant Group（蚂蚁集团蚂蚁医疗）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
该研究针对高分辨率天气预报的 data bottleneck，提出BaguanHR框架，通过变量级超分辨率合成高分辨率数据，使预报性能超越现有方法，且数据量与预报误差呈幂律关系。
COSYS-GRETTIA, Université Gustave Eiffel, IFSTTAR（古斯塔夫·埃菲尔大学 IFSTTAR COSYS-GRETTIA）；Polytechnique Montréal（蒙特利尔理工学院）；Interuniversitary Research Centre on Entreprise Networks, Logistics and Transport (CIRRELT)（校企网络、物流与运输跨大学研究中心）
One of the major goals of transport operators is to adapt the transport supply scheduling to the passenger demand for existing transport networks during each specific period. Another problem mentioned by operators is accurately estimating the demand for disposable ticket or pass to adapt ticket availability to passenger demand. In this context, we propose generic data shaping, allowing the use of well-known regression models (basic, statistical and machine learning models) for the long-term forecasting of passenger demand with fine-grained temporal resolution. Specifically, this paper investig…
Forecasting-based Biomedical Time-series Data Synthesis for Open Data and Robust AI
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea（韩国成均馆大学电气工程学院）；Department of Applied AI, Hansung University, Seoul, Republic of Korea（汉城市立大学应用人工智能系）
SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring
Hamilton Institute（汉密尔顿研究所）；Insight Centre for Data Analytics（洞察数据分析中心）；Maynooth University（梅努斯大学）；University of León（莱昂大学）；Dublin City University（都柏林城市大学）
University of Pisa（比萨大学）；ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）；Northeastern University（东北大学）
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that prov…
Imperial College London（帝国理工学院）；IBM Research（IBM研究院）
Distributed computing systems often consist of hundreds of nodes, executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives. First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting m…
The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less dense for long-term traffic predictions. The transport literature is also sparse when it comes to city-scale traffic predictions, mainly because of low data availability. In this work, we report an effort to investigate whether deep learning models can be useful for the long-term large-scale traffic prediction task, while focusing on the scalability of the models. We investigate a city-scale traffic dataset with 14 weeks of speed observations collected every 15 minutes over 1098 segm…
Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning
The School of Natural and Computing Sciences and the Interdisciplinary Institute at the University of Aberdeen（阿伯丁大学自然科学与计算科学学院及跨学科研究所）；UiT The Arctic University of Norway（挪威北极大学）
Learning Data-Driven Uncertainty Set Partitions for Robust and Adaptive Energy Forecasting with Missing Data
Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院电子与电气工程系）；Center for Processes, Renewable Energy and Energy Systems (PERSEE), Mines Paris, PSL University（进程、可再生能源与能源系统中心（PERSEE），巴黎 Mines，PSL 大学）
It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize …
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing prev…
