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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

Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method

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arXiv 2017年

作者： Lourenço, Bruno F. Fukushima, Masao Fukuda, Ellen H. Department of Computer and Information Science Faculty of Science and Technology Seikei University Tokyo180-8633 Japan Department of Applied Mathematics and Physics Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Department of Systems and Mathematical Science Faculty of Science and Engineering Nanzan University NagoyaAichi466-8673 Japan

In this work we are interested in nonlinear symmetric cone problems (NSCPs), which contain as special cases nonlinear semidefinite programming, nonlinear second order cone programming and the classical nonlinear programming problems. We explore the possibility of reformulating NSCPs as common nonlinear programs (NLPs), with the aid of squared slack variables. Through this connection, we show how to obtain second order optimality conditions for NSCPs in an easy manner, thus bypassing a number of difficulties associated to the usual variational analytical approach. We then discuss several aspects of this connection. In particular, we show a "sharp" criterion for membership in a symmetric cone that also encodes rank information. Also, we discuss the possibility of importing convergence results from nonlinear programming to NSCPs, which we illustrate by discussing a simple augmented Lagrangian method for nonlinear symmetric cones. We show that, employing the slack variable approach, we can use the results for NLPs to prove convergence results, thus extending a special case (i.e., the case with strict complementarity) of an earlier result by Sun, Sun and Zhang for nonlinear semidefinite programs.90C46, 90C30 Copyright © 2017, The Authors. All rights reserved.

关键词： Nonlinear programming

Overview of KAGRA : Data transfer and management

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Progress of Theoretical and Experimental Physics 2023年第10期2023卷

作者： Akutsu, T Ando, M Arai, K Arai, Y Araki, S Araya, A Aritomi, N Asada, H Aso, Y Bae, S Bae, Y Baiotti, L Bajpai, R Barton, M A Cannon, K Cao, Z Capocasa, E Chan, M Chen, C Chen, K Chen, Y Chiang, C-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, G Ha, S Hagiwara, A Haino, S Han, W-B Hasegawa, K Hattori, K Hayakawa, H Hayama, K Himemoto, Y Hiranuma, Y Hirata, N Hirose, E Hong, Z Hsieh, B Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y Hui, D C Y Ide, S Ikenoue, B Imam, S Inayoshi, K Inoue, Y Ioka, K Ito, K Itoh, Y Izumi, K Jeon, C Jin, H-B Jung, K Jung, P Kaihotsu, K Kajita, T Kakizaki, M Kamiizumi, M Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y Kojima, Y Kokeyama, K Komori, K Kong, A K H Kotake, K Kozakai, C Kozu, R Kumar, R Kume, J Kuo, C Kuo, H-S Kuromiya, Y Kuroyanagi, S Kusayanagi, K Kwak, K Lee, H K Lee, H W Lee, R Leonardi, M Li, K L Lin, L C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H L Liu, G C Luo, L-W Majorana, E Marchio, M Michimura, Y Mio, N Miyakawa, O Miyamoto, A Miyazaki, Y Miyo, K Miyoki, S Mori, Y Morisaki, S Moriwaki, Y Nagano, K Nagano, S Nakamura, K Nakano, H Nakano, M Nakashima, R Nakayama, Y Narikawa, T Naticchioni, L Negishi, R Quynh, L Nguyen Ni, W-T Nishizawa, A Nozaki, S Obuchi, Y Ogaki, W Oh, J J Oh, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, Y Sato, S Sato, T Sawada, T Sekiguchi, T Sekiguchi, Y Shao, L Shibagaki, S Shimizu, R Shimoda, T Shimode, K Shinkai, H Shishido, T Shoda, A Somiya, K Son, E J Sotani, H Sugimoto, R Suresh, J Suzuki, T Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University No. 19 Xinjiekou Street Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

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The simulation of multilayer magnetic films

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arXiv 2017年

作者： Kapitan, V.Yu. Perzhu, A.V. Nefedev, K.V. Department of Computer Systems School of Natural Sciences Far Eastern Federal University 8 Sukhanova St. Vladivostok690950 Institute of Applied Mathematics Far Eastern Branch Russian Academy of Science 7 Radio St. Vladivostok690041

The results of numerical calculations of the thermodynamic properties of multilayer films with alternating magnetic and non-magnetic layers were presented. computer simulation of such structures within the frame of the classical Heisenberg model was carried out by Monte Carlo methods. The processes of magnetization reversal of multilayer structures in external magnetic fields were investigated. Copyright © 2017, The Authors. All rights reserved.

关键词： Monte Carlo methods

Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans and Bayesian inference

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arXiv 2018年

作者： Lipková, Jana Angelikopoulos, Panagiotis Wu, Stephen Alberts, Esther Wiestler, Benedikt Diehl, Christian Preibisch, Christine Pyka, Thomas Combs, Stephanie Hadjidoukas, Panagiotis van Leemput, Koen Koumoutsakos, Petros Lowengrub, John Menze, Bjoern Germany Dept. of Neuroradiolog Klinikum Rechts der Isar TUM Germany D.E. Shaw Research L.L.C United States Institute of Statistical Mathematics Tokyo Japan Dept. of Diagnostic and Interventional Neuroradiology & Neuroimaging Center & Clinic for Neurology Klinikum Rechts der Isar TUM Germany Dept. of Nuclear Medicine Klinikum Rechts der Isar TUM Germany Dept. of Radiation Oncology Klinikum Rechts der Isar TUM Institute of Innovative Radiotherapy Helmholtz Zentrum Munich & Deutsches Konsortium für Translationale Krebsforschung Germany IBM Research - Zurich Switzerland Harvard Medical School Boston United States Dept. of Applied Mathematics and Computer Science TU Denmark Denmark Computational Science and Engineering Lab ETH Zürich Switzerland Dept. of Mathematics Biomedical Engineering Chemical Engineering and Materials Science Center for Complex Biological Systems Chao Family Comprehensive Cancer Center UC Irvine United States Institute for Advanced Study TUM Germany

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in glioblastoma patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and thus is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design. Copyright © 2018, The Authors. All rights reserved.

关键词： Radiotherapy

Technologies for the ELGAR large scale atom interferometer array

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arXiv 2020年

作者： Canuel, B. Abend, S. Amaro-Seoane, P. Badaracco, F. Beaufils, Q. Bertoldi, A. Bongs, K. Bouyer, P. Braxmaier, C. Chaibi, W. Christensen, N. Fitzek, F. Flouris, G. Gaaloul, N. Gaffet, S. Garrido Alzar, C.L. Geiger, R. Guellati-Khelifa, S. Hammerer, K. Harms, J. Hinderer, J. Holynski, M. Junca, J. Katsanevas, S. Klempt, C. Kozanitis, C. Krutzik, M. Landragin, A. Làzaro Roche, I. Leykauf, B. Lien, Y.-H. Loriani, S. Merlet, S. Merzougui, M. Nofrarias, M. Papadakos, P. Pereira dos Santos, F. Peters, A. Plexousakis, D. Prevedelli, M. Rasel, E.M. Rogister, Y. Rosat, S. Roura, A. Sabulsky, D.O. Schkolnik, V. Schlippert, D. Schubert, C. Sidorenkov, L. Siemß, J.-N. Sopuerta, C.F. Sorrentino, F. Struckmann, C. Tino, G.M. Tsagkatakis, G. Viceré, A. von Klitzing, W. Woerner, L. Zou, X. LP2N Laboratoire Photonique Numérique et Nanosciences Université Bordeaux-IOGS-CNRS:UMR 5298 rue F. Mitterrand TalenceF-33400 France Leibniz Universität Hannover Institut für Quantenoptik Welfengarten 1 HannoverD-30167 Germany 08193 Spain Carrer del Gran Capità 2-4 Edifici Nexus despatx 201 Barcelona08034 Spain Kavli Institute for Astronomy and Astrophysics Beijing100871 China Institute of Applied Mathematics Academy of Mathematics and Systems Science CAS Beijing100190 China Zentrum für Astronomie und Astrophysik TU Berlin Hardenbergstraße 36 Berlin10623 Germany L’AquilaI-67100 Italy INFN Laboratori Nazionali del Gran Sasso AssergiI-67100 Italy LNE-SYRTE Observatoire de Paris Université PSL CNRS Sorbonne Université 61 avenue de l’Observatoire PARISF-75014 France Midlands Ultracold Atom Research Centre School of Physics and Astronomy University of Birmingham BirminghamB15 2TT United Kingdom ZARM Unversity of Bremen Am Fallturm 2 Bremen28359 Germany DLR German Aerospace Center Linzer Strasse 1 Bremen28359 Germany ARTEMIS Université Côte d’Azur Observatoire de la Côte d’Azur CNRS NiceF-06304 France Leibniz University Hannover Appelstrasse 2 Hannover30167 Germany Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion70013 Greece LSBB Laboratoire Souterrain Bas Bruit CNRS Avignon University - La grande combe Rustrel84400 France Laboratoire Kastler Brossel Sorbonne Université CNRS ENS-PSL Research University Collège de France 4 place Jussieu Paris75005 France Institut de Physique du Globe de Strasbourg UMR 7516 Université de Strasbourg/EOST CNRS 5 rue Descartes Strasbourg67084 France I-56021 Italy Humboldt-Universität zu Berlin Institute of Physics Newtonstrasse 15 Berlin12489 Germany Computer Science Department University of Crete Heraklion70013 Greece Dept. of Physics and Astronomy Univ. of Bologna Via Berti-Pichat 6/2 BolognaI-40126 Italy Söflinger Str. 100 Ulm

We proposed the European Laboratory for Gravitation and Atom-interferometric Research (ELGAR), an array of atom gradiometers aimed at studying space-time and gravitation with the primary goal of observing gravitational waves (GWs) in the infrasound band with a peak strain sensitivity of 3.3×10−22/√Hz at 1.7 Hz. In this paper we detail the main technological bricks of this large scale detector and emphasis the research pathways to be conducted for its realization. We discuss the site options, atom optics, and source requirements needed to reach the target sensitivity. We then discuss required seismic isolation techniques, Gravity Gradient Noise reduction strategies, and the metrology of various noise couplings to the detector. Copyright © 2020, The Authors. All rights reserved.

关键词： Atoms

Isochronous dynamics in pulse coupled oscillator networks with delay

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arXiv 2017年

作者： Li, Pan Lin, Wei Efstathiou, Konstantinos School of Mathematical Sciences Centre for Computational Systems Biology of ISTBI Fudan University Shanghai200433 China Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen P.O. Box 407 AK Groningen9700 Netherlands Shanghai Key Laboratory of Contemporary Applied Mathematics LMNS Ministry of Education China

We consider a network of identical pulse-coupled oscillators with delay and all-to-all coupling. We demonstrate that the discontinuous nature of the dynamics induces the appearance of isochronous regions-subsets of the phase space filled with periodic orbits having the same period. For fixed values of the network parameters each such isochronous region corresponds to a subset of initial states on an appropriate surface of section with non-zero dimension such that all periodic orbits in this set have qualitatively similar dynamical behaviour. We analytically and numerically study in detail such an isochronous region, give a proof of its existence, and describe its properties. We further describe other isochronous regions that appear in the system. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamics

P139 Automatic Classification of Arterial and Venular Trees in Colour Fundus Images

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Artery Research 2018年第1期24卷 119-119页

作者： ena Martinez-Perez, M. E. Parker, Kim Witt, Nick Thom, S. A. McG. Hughes, Alun Department of Computer Science Institute of Research in Applied Mathematics and Systems National Autonomous University of Mexico UK Department of Bioengineering Imperial College London UK Faculty of Medicine National Heart & Lung Institute Imperial College London UK Institute of Cardiovascular Science University College London London UK

Quantitative imaging of retinal arterioles and venules offers unique insights into cardiovascular and microvascular diseases but is laborious. We developed and tested a method to automatically identify Arte-rial/Venular (A/V) vessels in digital retinal images in conjunction with a semi-automatic segmentation technique. Segmentation of blood vessels and the Optic Disc (OD) was performed as previously described [1] using a dataset of X colour fundus images. Using the OD as a reference point a graph representation was constructed using the vessel skeletons. Vessel bifurcations and crossings were identified based on direction and local geometry, and A/V classification was carried out by fuzzy logic classification using colour information. Results were compared with expert classification. 157 arterial and 150 venular segments were classified. Preliminary Results showed sensitivity, specificity and accuracy of 42.20%, 99.21% and 97.73% for arteries and 50.89%, 98.70% and 97.54% for veins. An example is shown in Figure 1. computer-based systems can assess local and global aspects of the retinal microvascular architecture, geometry and topology. Automated A/V classification will facilitate efficient cost-effective assessment of clinical images at scale.

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Automatic classification of arterial and venular trees in colour fundus images

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Artery Research 2018年 24卷 119-119页

作者： M Elena Martinez-Perez Kim Parker Nick Witt S.A.McG. Thom Alun Hughes Department of Computer Science Institute of Research in Applied Mathematics and Systems National Autonomous University of Mexico UK Department of Bioengineering Imperial College London UK Faculty of Medicine National Heart & Lung Institute Imperial College London UK Institute of Cardiovascular Science University College London London UK