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6G Positioning and Sensing Through the Lens of Sustainability, Inclusiveness, and Trustworthiness

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arXiv 2023年

作者： Wymeersch, Henk Chen, Hui Guo, Hao Keskin, Musa Furkan Khorsandi, Bahare M. Moghaddam, Mohammad H. Ramirez, Alejandro Schindhelm, Kim Stavridis, Athanasios Svensson, Tommy Yajnanarayana, Vijaya Department of Electrical Engineering Chalmers University of Technology Sweden Electrical and Computer Engineering Department New York University Tandon School of Engineering BrooklynNY United States Chalmers University of Technology Sweden Nokia Strategy and Technology Munich Germany QAMCOM Research and Technology AB Gothenburg Sweden Siemens’ corporate research unit in Munich Germany Ericsson Research Ericsson Sweden Ericsson Research India

6G promises a paradigm shift by integrating positioning and sensing, enhancing not only the communication performance but also enabling location- and context-aware services. Historically, positioning and sensing were focused on cost and performance tradeoffs, implying an escalated demand for resources, such as radio, physical, and computational resources, for improved performance. However, 6G expands this perspective, embracing a set of broader values, namely sustainability, inclusiveness, and trustworthiness. From a joint industrial/academic perspective, this paper aims to shed light on these important value indicators and their relationship with the conventional key performance indicators in the context of positioning and sensing. © 2023, CC BY.

关键词： Sustainable development

Wielding Occam’s razor: Fast and frugal retail forecasting

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arXiv 2021年

作者： Petropoulos, Fotios Grushka-Cockayne, Yael Siemsen, Enno Spiliotis, Evangelos School of Management University of Bath United Kingdom Darden School of Business University of Virginia United States Wisconsin School of Business University of Wisconsin-Madison United States Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece

The algorithms available for retail forecasting have increased in complexity. Newer methods, such as machine learning, are inherently complex. The more traditional families of forecasting models, such as exponential smoothing and autoregressive integrated moving averages, have expanded to contain multiple possible forms and forecasting proles. We question the complexity of forecasting and the need to consider such large families of models. Our argument is that parsimoniously identifying suitable subsets of models will not decrease forecasting accuracy, nor will they reduce the ability to estimate forecast uncertainty. We propose a framework that balances forecasting performance versus computational cost. As a result, we consider a reduced set of models. We empirically demonstrate that such a reduced set performs well. Finally, we translate computational benefits to monetary cost savings and environmental impact and discuss the implications of our results in the context of large retailers. © 2021, CC BY.

关键词： forecasting

Hierarchical forecast reconciliation with machine learning

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arXiv 2020年

作者： Spiliotis, Evangelos Abolghasemi, Mahdi Hyndman, Rob J. Petropoulos, Fotios Assimakopoulos, Vassilios Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Department of Data Science & AI Monash University MelbourneVIC3800 Australia Department of Econometrics and Business Statistics Monash University MelbourneVIC3800 Australia School of Management University of Bath Bath United Kingdom Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece

Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns. Copyright © 2020, The Authors. All rights reserved.

关键词： forecasting

Déjà vu: forecasting with similarity

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arXiv 2019年

作者： Kang, Yanfei Spiliotis, Evangelos Petropoulos, Fotios Athiniotis, Nikolaos Li, Feng Assimakopoulos, Vassilios School of Economics and Management Beihang University Beijing China Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece School of Management University of Bath United Kingdom

Accurate forecasts are vital for supporting the decisions of modern companies. To improve statistical forecasting performance, forecasters typically select the most appropriate model for each given time series. However, statistical models usually presume some data generation process, while making strong distributional assumptions about the errors. In this paper, we present a new approach to time series forecasting that relaxes these assumptions. A target series is forecasted by identifying similar series from a reference set (déjà vu). Instead of extrapolating, the future paths of the similar reference series are aggregated and serve as the basis for the forecasts of the target series. "forecasting with similarity" is a data-centric approach that tackles model uncertainty without depending on statistical forecasting models. We offer definitions for deriving both the point forecasts and the corresponding prediction intervals. We evaluate the approach using a rich collection of real data and show that it results in good forecasting accuracy, especially for yearly series. Finally, while traditional statistical approaches underestimate the uncertainty around the forecasts, our approach results in upper coverage levels that are much closer to the nominal values. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

forecasting: theory and practice

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： forecasting

Determinants of domestic electricity consumption and energy behavior: A Greek case study

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Determinants of domestic electricity consumption and energy ...

International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Konstantina Desipri Nikoletta Zampeta Legaki Vassilis Assimakopoulos Forecasting & Strategy Unit Decision Support Systems Laboratory NTUA School of Electrical & Computer Engineering Athens Greece

Households constitute an important target group for energy conservation. Understanding the occupant's behavior and defining the factors that considerable affect the electricity consumption will contribute to improving the energy performance of buildings. An online survey was therefore conducted and 221 questionnaires were completed to provide the necessary data. The present study links the demographics and dwelling characteristics to electricity consumption and energy behavior using chi-square based indexes and Pearson distances. The results revealed that associations exist and a grouping of cases was performed using the K-means clustering algorithm in order to provide consumption patterns.

关键词： Electricity Home appliances Energy efficiency Buildings Energy consumption Correlation Cities and towns

How difficult is it to forecast the world's most successful products?: The case of the Apple iPhone

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How difficult is it to forecast the world's most successful ...

54th Conference of the Operational Research Society 2012, OR54

作者： Fotios, Petropoulos Konstantinos, Nikolopoulos Akrivi, Litsa Yiannis, Polychronakis Vassilios, Assimakopoulos Lancaster Centre for Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom Bangor Business School Bangor University Bangor United Kingdom Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece University of Salford Manchester United Kingdom

ISBN: (纸本)0903440512

Apple is the largest publicly traded company in the world by market capitalisation, and also the largest technology company in the world by revenue and profit. Accurate forecasting unit sales is of major importance as even the smallest of errors will have a huge impact in terms of sales management and revenues. The current research investigates extrapolation alternatives relating to iPhone unit sales, probably the corporation's most influential product. Simple and straightforward time series techniques (Naïve, Exponential Smoothing methods, Theta model) and competing judgmental approaches (Unaided Judgment, Interaction Groups) performed by three groups of participants in terms of expertise (novices, semi-experts, experts). Moreover, two subgroups (for each level of expertise) were formed, regarding the amount of information provided to participants. The performance of the alternatives was measured in terms of bias and accuracy, using simple error and absolute percentage error. Results indicate that judgmental approaches produced larger forecasts than quantitative methods, whilst focusing effect is verified to lead to cognitive bias when forecasting future outcomes. As far as performance is concerned, a combination of time series extrapolation techniques and judgmental forecasts derived from groups with limited knowledge produces the smallest forecasting errors.

关键词： forecasting

Proceedings of the 8th Annual Conference on the Science of Dissemination and Implementation : Washington, DC, USA. 14-15 December 2015

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Implementation science : IS 2016年第2期11 Suppl 2卷 100页

作者： Chambers, David Simpson, Lisa Hill-Briggs, Felicia Neta, Gila Vinson, Cynthia Beidas, Rinad Marcus, Steven Aarons, Gregory Hoagwood, Kimberly Schoenwald, Sonja Evans, Arthur Hurford, Matthew Rubin, Ronnie Hadley, Trevor Barg, Frances Walsh, Lucia Adams, Danielle Mandell, David Martin, Lindsey Mignogna, Joseph Mott, Juliette Hundt, Natalie Kauth, Michael Kunik, Mark Naik, Aanand Cully, Jeffrey McGuire, Alan White, Dominique Bartholomew, Tom McGrew, John Luther, Lauren Rollins, Angie Salyers, Michelle Cooper, Brittany Funaiole, Angie Richards, Julie Lee, Amy Lapham, Gwen Caldeiro, Ryan Lozano, Paula Gildred, Tory Achtmeyer, Carol Ludman, Evette Addis, Megan Marx, Larry Bradley, Katharine VanDeinse, Tonya Wilson, Amy Blank Stacey, Burgin Powell, Byron Bunger, Alicia Cuddeback, Gary Barnett, Miya Stadnick, Nicole Brookman-Frazee, Lauren Lau, Anna Dorsey, Shannon Pullmann, Michael Mitchell, Shannon Schwartz, Robert Kirk, Arethusa Dusek, Kristi Oros, Marla Hosler, Colleen Gryczynski, Jan Barbosa, Carolina Dunlap, Laura Lounsbury, David O’Grady, Kevin Brown, Barry Damschroder, Laura Waltz, Thomas Ritchie, Mona Atkins, David Imel, Zac E. Xiao, Bo Can, Doğan Georgiou, Panayiotis Narayanan, Shrikanth Berkel, Cady Gallo, Carlos Sandler, Irwin Brown, C. Hendricks Wolchik, Sharlene Mauricio, Anne Marie Mehrotra, Sanjay Chandurkar, Dharmendra Bora, Siddhartha Das, Arup Tripathi, Anand Saggurti, Niranjan Raj, Anita Hughes, Eric Jacobs, Brian Kirkendall, Eric Loeb, Danielle Trinkley, Katy Yang, Michael Sprowell, Andrew Nease, Donald Lyon, Aaron Lewis, Cara Boyd, Meredith Melvin, Abigail Nicodimos, Semret Liu, Freda Jungbluth, Nathanial Flynn, Allen Landis-Lewis, Zach Sales, Anne Baloh, Jure Ward, Marcia Zhu, Xi Bennett, Ian Unutzer, Jurgen Mao, Johnny Proctor, Enola Vredevoogd, Mindy Chan, Ya-Fen Williams, Nathaniel Green, Phillip Bernstein, Steven Rosner, June-Marie DeWitt, Michelle Tetrault, Jeanette Dziura, James Hsiao, Allen Sussman, Scott O’Connor, Patrick Toll, Benjamin Jones, Michael Gassaway, Julie Tobin, Jonathan Zatzic Division of Cancer Control and Population Sciences National Cancer Institute National Institutes of Health Rockville USA AcademyHealth Washington USA Department of Medicine and Welch Center for Prevention Epidemiology and Clinical Research Johns Hopkins Medical Institutions Baltimore USA Psychiatry University of Pennsylvania Philadelphia USA Family Medicine and Community Health University of Pennsylvania Philadelphia USA School of Social Policy and Practice University of Pennsylvania Philadelphia USA Psychiatry UC San Diego La Jolla USA Child & Adolescent Psychiatry The Child Study Center at NYU Langone Medical Center New York NY New York USA Psychiatry and Behavioral Sciences MUSC Charleston USA DBHIDS City of Philadelphia Philadelphia USA CBH City of Philadelphia Philadelphia USA Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia USA Health Services Research & Development Department of Veterans Affairs & Baylor College of Medicine Houston USA Treatment Core Department of Veterans Affairs Waco USA National Center for PTSD Executive Division Department of Veterans Affairs White River Junction USA HSR&D HSR&D Center for Health Information and Communication Roudebush VA Medical Center Indianapolis USA Psychology Indiana University Purdue University Indianapolis Indianapolis USA Department of Psychiatric Rehabilitation and Counseling Rutgers University Newark USA Health Services Research & Development Richard L. Roudebush VAMC Indianapolis USA IUPUI Department of Psychology ACT Center of Indiana Indianapolis USA Human Development Washington State University Pullman USA Prevention Science Washington State University Pullman USA Group Health Research Institute Group Health Seattle USA Behavioral Health Services Group Health Seattle USA Preventive Care Group Health Seattle USA Health Services Research & Development Primary and Specialty Medical Care Service VA Puget Sound Seattle USA School of Social Work Un

A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing

关键词： Veteran Affair Motivational Interview Qualitative Comparative Analysis Community That Care Supplemental Nutrition Assistance Program

A Neural Network Approach for the Theta Model

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A Neural Network Approach for the Theta Model

2012 International Conference on Information engineering(ICIE 2012)

作者： Constantinidou Christina Nikolopoulos Konstantinos Bougioukos Nikolaos Tsiafa Eirini Petropoulos Fotios Assimakopoulos Vassilios Forecasting & Strategy Unit School of Electrical and Computer EngineeringNational Technical University of AthensHeroon Polytechniou 915780 ZografouGreece The Business School Bangor UniversityBangorGwynedd LL57 2DGU.K.

Classic Theta model decomposes the original data series into two separate lines,which are extrapolated separately and the forecasts are combined with equal *** current study explores a neural network approach to Theta model,in terms of optimizing the combination weights of the two components in the final *** performance of the proposed method (Theta AI) is compared against the original method for the two subsets of the NN3 forecasting competition,which primary objective was the evaluation of methods using neural networks or artificial intelligence for time series *** indicate that the new approach is very promising towards the generalization of the Theta model.

关键词： Time Series forecasting,Theta Model,Neural Networks,Artificial Intelligence,forecasting Competitions,Theta AI