22 papers · ranked by Valyu relevance
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, M. Zied Babaï + 76 more
'M. Zied Babaï' 'Devon K. Barrow' 'Souhaib Ben Taieb' 'Christoph Bergmeir' 'Ricardo J. Bessa' 'Jakub Bijak' 'John E. Boylan' 'Jethro Browell' 'Claudio Carnevale' 'Jennifer L. Castle' 'Pasquale Cirillo' 'Michael P. Clements' 'Clara Cordeiro' 'Fernando Luiz Cyrino Oliveira' 'Shari De Baets' 'Alexander Dokumentov' 'Joanne…
Owen L Petchey, Mikael Pontarp, Thomas M Massie, Sonia Kéfi + 15 more
Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other…
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…
Stephen A. Lauer, Alexandria Brown, Nicholas G Reich
Forecasting transmission of infectious diseases, especially for vector-borne diseases, poses unique challenges for researchers. Behaviors of and interactions between viruses, vectors, hosts, and the environment each play a part in determining the transmission of a disease. Public health surveillance systems and other…
Maximilian Zellner, Ali E. Abbas, David V. Budescu, Aram Galstyan
This paper's top-level goal is to provide an overview of research conducted in the many academic domains concerned with forecasting. By providing a summary encompassing these domains, this survey connects them, establishing a common ground for future discussions. To this end, we survey literature on human judgement and…
Chelsea S. Lutz, Mimi P. Huynh, Monica Schroeder, Sophia Anyatonwu + 14 more
'F. Scott Dahlgren' 'Gregory Danyluk' 'Danielle Fernandez' 'Sharon K. Greene' 'Nodar Kipshidze' 'Leann Liu' 'Osaro Mgbere' 'Lisa A. McHugh' 'Jennifer F. Myers' 'Alan Siniscalchi' 'Amy D. Sullivan' 'Nicole West' 'Michael A. Johansson' 'Matthew Biggerstaff'] Background Infectious disease forecasting aims to predict…
Owen L. Petchey, Mikael Pontarp, Thomas M. Massie, Sonia Kéfi + 14 more
Forecasts of how ecological systems respond to environmental change are increasingly important. Sufficiently inaccurate forecasts will be of little use, however. For example, weather forecasts are for about one week into the future; after that they are too unreliable to be useful (i.e., the forecast horizon is about…
Stefan Siegert, Jochen Broecker, Hölger Kantz
We compare probabilistic predictions of extreme temperature anomalies issued by two different forecast schemes. One is a dynamical physical weather model, the other a simple data model. We recall the concept of skill scores in order to assess the performance of these two different predictors. Although the result…
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…
Ethan P. White, Glenda M. Yenni, Shawn D. Taylor, Erica M. Christensen + 3 more
Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts…
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…
Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe + 2 more
Real-time forecasts based on mathematical models have become increasingly important to help guide critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and it has not been established what the best metrics for assessment are. Here, we…
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…
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…
Christian Kiaer, Stefan Neuenfeldt, Mark R. Payne
Forecasting variation in the recruitment to fish stocks is one of the most challenging and long-running problems in fisheries science and essentially remains unsolved today. Traditionally recruitment forecasts are developed and evaluated based on explanatory and goodness-of-fit approaches that do not reflect their…
Silvia Jordan, Martin Messner
Accounting studies have analyzed rolling forecasts and similar dynamic approaches to planning as a way to improve the quality of planning. We complement this research by investigating an alternative (complementary) way to improve planning quality, i.e. the use of forecast accuracy indicators as a results control…
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…
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…
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)…
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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…
Authors not listed
The rapid advancements in computational methods have revolutionized drug discovery and development. These methods, ranging from molecular modelling to machine learning algorithms, have drastically increased in number and sophistication. However, a comprehensive understanding of these diverse approaches is essential for…