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Related papers: Coherent forecasting of NoGeAR(1) model

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This paper introduces a new type of regression methodology named as Convex-Area-Wise Linear Regression(CALR), which separates given datasets by disjoint convex areas and fits different linear regression models for different areas. This…

Databases · Computer Science 2024-06-11 Bohan Lyu , Jianzhong Li

Accurate estimation of order fulfillment time is critical for e-commerce logistics, yet traditional rule-based approaches often fail to capture the inherent uncertainties in delivery operations. This paper introduces a novel framework for…

Machine Learning · Computer Science 2025-08-04 Tinghan Ye , Amira Hijazi , Pascal Van Hentenryck

In this paper, we discuss the convergence of an Algebraic MultiGrid (AMG) method for general symmetric positive-definite matrices. The method relies on an aggregation algorithm, named \emph{coarsening based on compatible weighted matching},…

Numerical Analysis · Mathematics 2023-07-18 Pasqua D'Ambra , Fabio Durastante , Salvatore Filippone , Ludmil Zikatanov

Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally while producing series-specific forecasts locally are now…

Machine Learning · Computer Science 2024-05-14 Abishek Sriramulu , Christoph Bergmeir , Slawek Smyl

Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure…

Machine Learning · Computer Science 2024-12-18 Aoming Liang , Qi Liu , Lei Xu , Fahad Sohrab , Weicheng Cui , Changhui Song , Moncef Gabbouj

Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applications, may be predicted with reasonable accuracy. We refer…

Machine Learning · Computer Science 2023-08-01 Jimeng Shi , Rukmangadh Myana , Vitalii Stebliankin , Azam Shirali , Giri Narasimhan

We propose a computationally efficient estimator, formulated as a convex program, for a broad class of non-linear regression problems that involve difference of convex (DC) non-linearities. The proposed method can be viewed as a significant…

Machine Learning · Statistics 2019-04-01 Sohail Bahmani

We propose a novel multi-task method for quantile forecasting with shared Linear layers. Our method is based on the Implicit quantile learning approach, where samples from the Uniform distribution $\mathcal{U}(0, 1)$ are reparameterized to…

Machine Learning · Computer Science 2022-12-07 Shayan Jawed , Lars Schmidt-Thieme

We consider a setting where multiple entities inter-act with each other over time and the time-varying statuses of the entities are represented as multiple correlated time series. For example, speed sensors are deployed in different…

Machine Learning · Computer Science 2021-03-23 Razvan-Gabriel Cirstea , Chenjuan Guo , Bin Yang

Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting that brings new challenges to uncertainty quantification. We…

Machine Learning · Statistics 2023-06-06 Margaux Zaffran , Aymeric Dieuleveut , Julie Josse , Yaniv Romano

This work addresses the challenge of ignition timing and load control in homogeneous charge compression ignition engines operating subject to uncertainty from complex combustion dynamics and external disturbances. To handle this issue, we…

Systems and Control · Electrical Eng. & Systems 2026-04-15 Xu Chen , Kevin Kluge , Maximilian Basler , Lorenz Dörschel , Heike Vallery

Constructing prediction intervals for time series forecasting is challenging, particularly when practitioners rely solely on point forecasts. While previous research has focused on creating increasingly efficient intervals, we argue that…

Methodology · Statistics 2025-01-20 Carlos Sebastián , Carlos E. González-Guillén , Jesús Juan

Predictive linear and nonlinear models based on kernel machines or deep neural networks have been used to discover dependencies among time series. This paper proposes an efficient nonlinear modeling approach for multiple time series, with a…

Machine Learning · Computer Science 2023-10-02 Kevin Roy , Luis Miguel Lopez-Ramos , Baltasar Beferull-Lozano

In several disciplines it is common to find time series measured at irregular observational times. In particular, in astronomy there are a large number of surveys that gather information over irregular time gaps and in more than one…

Instrumentation and Methods for Astrophysics · Physics 2021-05-12 Felipe Elorrieta , Susana Eyheramendy , Wilfredo Palma , Cesar Ojeda

Modern technological advances have enabled an unprecedented amount of structured data with complex temporal dependence, urging the need for new methods to efficiently model and forecast high-dimensional tensor-valued time series. This paper…

Methodology · Statistics 2023-09-28 Di Wang , Yao Zheng , Guodong Li

Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice. This paper proposes a community network vector autoregressive (CNAR) model, which utilizes the network structure to characterize…

Methodology · Statistics 2020-07-13 Elynn Y. Chen , Jianqing Fan , Xuening Zhu

In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very…

Machine Learning · Computer Science 2024-02-01 Matteo Fontana , Gianluca Zeni , Simone Vantini

Recent advancements in remote sensing technology and the increasing size of satellite constellations allows massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The…

Computation · Statistics 2021-05-11 Si Cheng , Bledar A. Konomi , Jessica L. Matthews , Georgios Karagiannis , Emily L. Kang

In many applications it is desirable to infer coarse-grained models from observational data. The observed process often corresponds only to a few selected degrees of freedom of a high-dimensional dynamical system with multiple time scales.…

Statistics Theory · Mathematics 2015-05-06 Serafim Kalliadasis , Sebastian Krumscheid , Grigorios A. Pavliotis

As global warming increases the complexity of weather patterns; the precision of weather forecasting becomes increasingly important. Our study proposes a novel preprocessing method and convolutional autoencoder model developed to improve…

Machine Learning · Computer Science 2024-11-11 Yo-Hwan Choi , Seon-Yu Kang , Minjong Cheon