Affective Forecasting is an psychology task that involves predicting an individual's future emotional responses, often hampered by reliance on external factors leading to inaccuracies, and typically remains at a qualitative analysis stage. To address these challenges, we narrows the scope of Affective Forecasting by introducing the concept of Human-interaction-based Emotion Forecasting (EF). This task is set within the context of a two-party interaction, positing that an individual's emotions are significantly influenced by their interaction partner's emotional expressions and informational cues. This dynamic provides a structured perspective for exploring the patterns of emotional change, thereby enhancing the feasibility of emotion forecasting.
Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance
LIVIA, Department of Software and IT Engineering（LIVIA，软件与IT工程系）；École de technologie supérieure (ÉTS)（高等技术学院）；Center for Informatics (CIn)（信息学中心）；Universidade Federal de Pernambuco (UFPE)（伯南布哥联邦大学）
University of Southern California（南加州大学）；J.P.Morgan AI Research（摩根大通人工智能研究院）
In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen s…
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
University of Minho（米尼奥大学）；Universidade do Porto（波尔图大学）；Laboratory for Artificial Intelligence and Computer Science (LIACC)（人工智能与计算机科学实验室（LIACC））；Fraunhofer Portugal AICOS（弗劳恩霍夫葡萄牙AICOS机构）；INESCTEC；University of Coimbra（科英布拉大学）
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR China（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系）；School of Computer and Information Sciences, University of Newcastle, Newcastle, Australia（新castle大学计算机与信息科学学院）；Hangzhou Innovation Institute of Beihang University, Hangzhou, China（北京航空航天大学杭州创新研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for futur…
Deep neural networks have demonstrated superior performance in short-term traffic forecasting. However, most existing traffic forecasting systems assume that the training and testing data are drawn from the same underlying distribution, which limits their practical applicability. The NeurIPS 2021 Traffic4cast challenge is the first of its kind dedicated to benchmarking the robustness of traffic forecasting models towards domain shifts in space and time. This technical report describes our solution to this challenge. In particular, we present a multi-task learning framework for temporal and spa…
University of Cambridge（剑桥大学）；ENGIE Lab CRIGEN（昂吉集团CRIGEN实验室）
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels. However, current approaches to modelling the cloud cover dynamics from sky images still lack precision regarding the spatial configuration of clouds, their temporal dynamics and physical interactions with solar radiation. Benefiting from a growing number of large datasets, data driven methods are being developed to address these limitations with prom…
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）
Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Models
Sunset High School（Sunset 高中）；Woodbridge High School（Woodbridge 高中）；Western Academy of Beijing（北京西方学院）；Computer Science Department, Metropolitan College（Metropolitan College 计算机科学系）
University of Rochester（罗切斯特大学）；Adobe Research（奥多比研究中心）；Snapchat Inc（Snapchat公司）
Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Co…
Recent developments in speech emotion recognition (SER) often leverage deep neural networks (DNNs). Comparing and benchmarking different DNN models can often be tedious due to the use of different datasets and evaluation protocols. To facilitate the process, here, we present the Speech Emotion Recognition Adaptation Benchmark (SERAB), a framework for evaluating the performance and generalization capacity of different approaches for utterance-level SER. The benchmark is composed of nine datasets for SER in six languages. Since the datasets have different sizes and numbers of emotional classes, …
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）
Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
Karlsruhe Institute of Technology, Institute for Industrial Production（卡尔斯鲁厄理工学院，工业生产研究所）；University of Duisburg-Essen, House of Energy Markets and Finance（杜伊斯堡-埃森大学，能源市场与金融中心）；Karlsruhe Institute of Technology, Institute for Automation and Applied Informatics（卡尔斯鲁厄理工学院，自动化与应用信息研究所）；Karlsruhe Institute of Technology, Institute of Statistics（卡尔斯鲁厄理工学院，统计研究所）；Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；Marburg University, Department of Mathematics and Computer Science（马尔堡大学，数学与计算机科学系）；Volatile GmbH（Volatile GmbH公司）；Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems – Jülich Systems Analysis（焦耳研究中心 GmbH，气候与能源系统研究所——焦耳系统分析）
PyPOTS Research（PyPOTS研究机构）；HKUST（香港科技大学）；King’s College London（伦敦国王学院）；University of Oxford（牛津大学）；Sichuan University（四川大学）；University of Washington（华盛顿大学）；GaTech（佐治亚理工学院）；HKUST(GZ)（香港科技大学（广州））；Northwest University（西北大学）；Squirrel AI（松鼠AI）
This paper analyses how Time Series Analysis techniques can be applied to capture movement of an exchange traded index in a stock market. Specifically, Seasonal Auto Regressive Integrated Moving Average (SARIMA) class of models is applied to capture the movement of Nifty 50 index which is one of the most actively exchange traded contracts globally [1]. A total of 729 model parameter combinations were evaluated and the most appropriate selected for making the final forecast based on AIC criteria [8]. NIFTY 50 can be used for a variety of purposes such as benchmarking fund portfolios, launching …
Towards Emotionally Consistent Text-Based Speech Editing: Introducing EmoCorrector and The ECD-TSE Dataset
Johns Hopkins University（约翰霍普金斯大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Inner Mongolia University（内蒙古大学）
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…
