Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts. Second, we benchmark ProbPNN with state-of-the-art benchmarks by comparing the achieved normalised continuous ranked probability score (nCRPS) and normalised Pinball Loss (nPL) on two data sets containing in total more than 1000 time series. The results of the benchmarks show that using statistical forecasting components improves the probabilistic forecast performance and that ProbPNN outperforms other deep learning forecasting methods whilst requiring less computation costs.
QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Models
Independent Researcher（独立研究者）；Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai)（信息行为脑机智能关键实验室）；School of Business and Management（商学院）
Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers
HCMC University of Technology and Education（胡志明市技术教育大学）；Indigo Technology Systems JSC（Indigo 科技系统股份公司）；International University-VNU-HCM（越南国立大学胡志明市国际大学）
National University of Science and Technology（国立科技大学）；Technical Universität Kaiserslautern（凯泽斯劳滕工业大学）；Deutsches Forschungszentrum für Künstliche Intelligenz（德国人工智能研究中心）
End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications. This is specifically true in time series domain where problems like disaster prediction, anomaly detection, and demand prediction often do not have a large amount of historical data. Moreover, relying purely on past examples for training can be sub-optimal since in doing so we ignore one very important domain i.e knowledge, which has its own distinct advantages. In this paper, we propose a novel kn…
Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Shandong University（山东大学）
Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
LIACC/Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院/LIACC研究所）；Institute of Mathematical and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学研究所）；Fraunhofer AICOS Portugal（弗劳恩霍夫葡萄牙AICOS研究所）
Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research
Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
DAMO Academy, Alibaba Group（阿里集团达摩院）；State Key Laboratory of Severe Weather Meteorological Science and Technology, Nanjing University（南京大学 severe weather meteorological science and technology 国家重点实验室）
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
University of New South Wales（新南威尔士大学）；HKGAI（香港人工智能学会）；Southern University of Science and Technology（南方科技大学）；The University of Tokyo（东京大学）；School of Artificial Intelligence of Jilin University（吉林大学人工智能学院）
Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
Faculty of Electronics and Telecommunications University of Engineering and Technology, VNU Hanoi, Vietnam（电子电信学院 工程技术大学 巴黎越南河内分校）；Faculty of Information Technology University of Engineering and Technology, VNU Hanoi, Vietnam（信息技术学院 工程技术大学 巴黎越南河内分校）
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…
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Pengcheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）
本文提出 CycleNet，通过残差周期预测（RCF）技术显式建模时间序列周期性并结合 Linear 或浅层 MLP 进行预测，在多领域实现最优精度且大幅减少参数，作为即插即用技术还能提升现有模型性能。
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）
In this work we improve forecasting of Sea Surface Height (SSH) and current velocity (speed and direction) in oceanic scenarios. We do so by resorting to Random Forests so as to predict the error of a numerical forecasting system developed for the Santos Channel in Brazil. We have used the Santos Operational Forecasting System (SOFS) and data collected in situ between the years of 2019 and 2021. In previous studies we have applied similar methods for current velocity in the channel entrance, in this work we expand the application to improve the SHH forecast and include four other stations in t…
TLCCSP: A Scalable Framework for Enhancing Time Series Forecasting with Time-Lagged Cross-Correlations
School of Artificial Intelligence（人工智能学院）；Institute of Artificial Intelligence and Future Networks（人工智能与未来网络研究所）；Department of Systems Science, Faculty of Arts and Sciences（艺术与科学学院系统科学系）；Guangdong Key Lab of AI and Multi-Modal Data Processing（广东人工智能与多模态数据处理重点实验室）；BNU-HKBU United International College（北师大-香港大学联合国际学院）
Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
National Meteorological Center of China Meteorological Administration（中国气象局国家气象中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究所）；China Meteorological Administration Training Center（中国气象局培训中心）
