23 papers · ranked by Valyu relevance
Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher + 291 more
'Andrea Brennen' 'Alvaro J. Castro Rivadeneira' 'Aaron Gerding' 'Tilmann Gneiting' 'Katie H. House' 'Yuxin Huang' 'Dasuni Jayawardena' 'Abdul H. Kanji' 'Ayush Khandelwal' 'Khoa Le' 'Anja Mühlemann' 'Jarad Niemi' 'Apurv Shah' 'Ariane Stark' 'Yijin Wang' 'Nutcha Wattanachit' 'Martha W. Zorn' 'Youyang Gu' 'Sansiddh Jain'…
Paul Goodwin, Jim Hoover, Spyros Makridakis, Fotios Petropoulos + 2 more
'Len Tashman' 'Afshan Naseem'] Reliable forecasts are key to decisions in areas ranging from supply chain management to capacity planning in service industries. It is encouraging then that recent decades have seen dramatic advances in forecasting methods which have the potential to significantly increase forecast…
Penelope Jones, Ulrich Stimming, Alpha Lee
Accurate forecasting of lithium-ion battery performance is important for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the R&D setting where cells are subjected to the same usage patterns, yet in practice there is great variability…
Richard Stromer, Oskar Triebe, Chad Zanocco, Ram Rajagopal
non-experts to create and understand their own forecasts Authors: ['Richard Stromer' 'Oskar Triebe' 'Chad Zanocco' 'Ram Rajagopal'] Forecasts inform decision-making in nearly every domain. Forecasts are often produced by experts with rare or hard to acquire skills. In practice, forecasts are often used by domain…
Mingyue Cheng, Xiaoyu Tao, Qi Liu, Ze Guo + 2 more
Time series forecasting has traditionally been formulated as a model-centric, static, and single-pass prediction problem that maps historical observations to future values. While this paradigm has driven substantial progress, it proves insufficient in adaptive and multi-turn settings where forecasting requires…
Dag Tjøstheim, Andrea Murari
Machine learning forecasting methods are compared to more traditional parametric statistical models. This comparison is carried out regarding a number of different situations and settings. A survey of the most used parametric models is given. Machine learning methods, such as convolutional networks, TCNs, LSTM…
Li Li, Yanfei Kang, Fotios Petropoulos, Feng Li
Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to…
Gerardo Chowell, Amanda Bleichrodt, Sushma Dahal, Amna Tariq + 3 more
To assess calibration and forecasting performance, we used four performance metrics: the mean absolute error (MAE), the mean squared error (MSE), the coverage of the 95% prediction intervals, and the weighted interval score (WIS) . While it is possible to generate h-time units ahead forecasts of an evolving process…
Shengjie Wang, Yanfei Kang, Fotios Petropoulos, Feng Li
In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial…
Joseph Hart, Hudson Smith, Lior Rennert, Christopher S. McMahan
Infectious disease transmission evolves as a dynamic process shaped by biological mechanisms, population behavior, and intervention policies, yet public health responses are often driven by lagging indicators. Accurate short- and long-term disease forecasting is essential for the timely deployment of intervention…
Mátyás Kocsis, Sándor Baran
In the last few years, AI-based models have become the centre of attention in weather forecasting due to their increasing accuracy and efficiency. Pioneering among weather services, ECMWF has developed its Artificial Intelligence Forecasting System (AIFS) model, which was first to provide data-driven ensemble forecasts…
Romana Limberger, Uriah Daugaard, Yves Choffat, Anubhav Gupta + 9 more
Accurate forecasts of ecological dynamics are critical for ecosystem management and conservation, yet the drivers of forecastability are poorly understood. Here we show that experiments are a powerful (but underutilized) tool to explore the limits of ecological forecasting. We conducted a long-term microcosm experiment…
Marieke Wesselkamp, James Albrecht, Ewan Pinnington, William J. Castillo + 2 more
relative system predictability Authors: ['Marieke Wesselkamp' 'James Albrecht' 'Ewan Pinnington' 'William J. Castillo' 'Florian Pappenberger' 'Carsten F. Dormann'] Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform…
Philip E. Bett, Hazel E. Thornton, Alberto Troccoli, Matteo De Felice + 4 more
'Matteo De Felice' 'Emma Suckling' 'Laurent Dubus' 'Yves-Marie Saint-Drenan' 'David J. Brayshaw'] Title: Graphical abstract
Takeshi Honda, Chinatsu Kozakai
Forecasting encounters between humans and large carnivores has largely relied on mechanistic models driven by causal factors such as food resources and weather. However, for short-term forecasting these approaches implicitly require unrealistically detailed real-time data on many covariates and an almost complete…
Zahrah Fayez Althobaiti
Predicting the economic implications of gas emissions and their repercussions is criti-cal to policymakers, especially given the current increasing trend in volume. Therefore, study on gas emission prediction is required. A hybrid model is proposed for forecasting CO_2_ emissions of Bahrain (BH) in this study. Singular…
Mingzhe Shi, Bahman Rostami-Tabar, Daniel Gartner
The ability to accurately forecast unscheduled care needs is of paramount importance for decision making in healthcare operations, ensuring a continuous and high-quality level of care. In this work, we provide a literature review of 156 research articles of forecasting applications with special focus on care services…
Malte C. Tichy
Assume that a grocery item is sold 1'234 times on a given day. What should an ideal forecast have predicted for such a well-selling item, on average? More generally, when considering a given outcome value, should the empirical average of forecasted expectation values for that outcome ideally match it? Many people will…
Jamie M Caldwell, Gang Liu, Erick Geiger, Scott F Heron + 13 more
Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of…
Christopher J. Brown, Christina Buelow, Rick D. Stuart-Smith, Neville Barrett + 2 more
Cascading human pressures and environmental change are affecting the natural dynamics of animal populations. Forecasting population abundances from time-series data provides an important avenue for testing competing ecological theories, and for supporting conservation planning and sustainable use, yet changing system…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
Authors not listed
Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…