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Journal of Forecasting

Volume 1, Issue 2

Article

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

INSEAD, 77305 Fontainebleau, France

A. Andersen, Department of Economic Statistics, The University of Sydney, New South Wales 2006, Australia

R. Carbone, Faculté des Sciences de l'Administration, Université Laval, Quebec, Canada G1K 7P4

R. Fildes, Manchester Business School, University of Manchester, Manchester M15 6PB, England

R. Lewandowski, Marketing Systems GMBH, Postfach 230109, Hunsruckstraße 9a, D‐4300 ESSEN 1 (Bredeney), West Germany

J. Newton and E. Parzen, Institute of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A.

R. Winkler, Indiana University, Graduate School of Business, Bloomington, IN 47405, U.S.A.

Spyros Makridakis is Professor of Management Science at INSEAD, France. He received his degree from the School of Industrial Studies in Greece and his M.B.A. and Ph.D. from New York University. He has published extensively in the areas of general systems and forecasting and has co‐authored Computer‐Aided Modeling for Managers (Addison‐Wesley, 1972), Forecasting Methods for Management, Third Edition (Wiley, 1979), Interactive Forecasting, Second Edition (Holden‐Day, 1978), and Forecasting: Methods and Applications (Wiley‐Hamilton. 1978). He has also been the co‐editor of TIMS Studies, Vol. 12, in Management Science. After his first degree in Statistics. Allan Andersen completed a Ph.D. in the Faculty of Economics at the University of Queensland. At present, he is a lecturer at the University of Sydney in the Department of Statistics. Allan's research interests lie in the general field of forecasting; in particular the time‐series approaches to the subject. He has published more than ten articles, and has co‐authored a book on non‐linear time‐series methods.

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INSEAD, 77305 Fontainebleau, France

A. Andersen, Department of Economic Statistics, The University of Sydney, New South Wales 2006, Australia

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INSEAD, 77305 Fontainebleau, France

Robert Carbone is the chairman of the Department of Management Sciences at La Faculte des Sciences de l'Administration de l'Université Laval. His Ph.D. is in urban and public affairs from Carnegie‐Mellon University. He is a member of AIDS, TIMS, and ORSA. His papers have appeared in The Journal of Environmental Systems, Management Science, IN FOR, Atmospheric Environment, and other journals. His principal current research interest is in data analysis and forecasting.

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INSEAD, 77305 Fontainebleau, France

R. Carbone, Faculté des Sciences de l'Administration, Université Laval, Quebec, Canada G1K 7P4

Robert Fildes is a lecturer in business forecasting at the Manchester Business School, University of Manchester. He received a Bachelor's degree (mathematics) from Oxford and a Ph.D. (statistics) from the University of California. He is co‐author of Forecasting for Business (Longmans, 1976) and an editor of Forecasting and Planning (Teakfield, 1978). He has also authored several articles on forecasting and applied statistics and served as a consultant in these fields. During 1978 he taught at the University of British Columbia and the University of California Berkeley.

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INSEAD, 77305 Fontainebleau, France

Michèle Hibon is currently a research assistant at INSEAD. For the last several years she has been working on various studies dealing with forecasting accuracy of time‐series methods. Before INSEAD, she was associated with the Ecole Polytechnique (Laboratoire de Recherche de Mecanique des Solides) as a computer specialist. She holds a degree in science and a diploma in advanced studies in physics.

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INSEAD, 77305 Fontainebleau, France

Rudolf Lewandowski, born 1938 in Valencia/Spain, studied mathematics and economics at the Sorbonne in Paris, and the universities of Bochum and Bonn and obtained degrees in both subjects. He came to the Federal Republic of Germany in 1962 as a mathematical adviser for a big French company. In 1966 he became the manager of the operations research, economics and marketing department in a leading software firm in West Germany, and founded in 1973‘MARKETING SYSTEMS’, and has been its general manager since. In 1973, he obtained a doctoral degree in economics at the Sorbonne. He has published papers on Markov processes, forecasting methodology and forecast systems, and is author of Prognose und Informationssysteme und ihre Anwendungen, 2 vols. (De Giuyter‐Verlag, Berlin 1974 and 1980), and La Prévision à Court Terme (Dunod, Paris 1979). He has lectured at various European universities, and is a member of several European Marketing Associations.

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INSEAD, 77305 Fontainebleau, France

Joseph Newton is an authority on the theory and algorithms of time series analysis particularly in the area of multivariate time series. His dissertation was written in 1975 at SUNY at Buffalo in the area of estimating the parameters of vector valued autoregressive moving average time series. His publications range from work on the asymptotic distribution of maximum likelihood estimators in multiple time series to the development of efficient algorithms for the prediction of time series. He has been a research assistant professor at SUNY at Buffalo and is currently an assistant professor in the Institute of Statistics at Texas A&M University.

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INSEAD, 77305 Fontainebleau, France

Emanuel Parzen is Distinguished Professor of Statistics at Texas A&M University. He has made research contributions to probability limit theorems, statistical spectral analysis by kernel methods, statistical communication theory, time series analysis by reproducing kernel Hilberg space methods, probability density estimation, multiple time series analysis, statistical spectral analysis by autoregressive methods, order determination criteria for autoregressive schemes, forecasting, and non‐parametric statistical data modelling using quantile and density‐quantile functions. He is the author of two widely used books: Modern Probability Theory and Its Applications (1960) and Stochastic Processes (1962).

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INSEAD, 77305 Fontainebleau, France

Robert Winkler is a distinguished professor of Quantitative Business Analysis in the Graduate School of Business, Indiana University, Bloomington, Indiana. For the 1980‐81 academic year he was a Visiting Professor at INSEAD, Fontainebleau, France. He received a B.S. from the University of Illinois and a Ph.D. from the University of Chicago, and he has held visiting positions at the University of Washington, the International Institute for Applied Systems Analysis, and Stanford University. He is the author of two books and numerous journal articles. His primary research interests involve probability forecasting, Bayesian inference, and statistical decision theory. He is a member of Tl MS and is currently serving as a Departmental Editor for Management Science.

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INSEAD, 77305 Fontainebleau, France

A. Andersen, Department of Economic Statistics, The University of Sydney, New South Wales 2006, Australia

R. Carbone, Faculté des Sciences de l'Administration, Université Laval, Quebec, Canada G1K 7P4

R. Fildes, Manchester Business School, University of Manchester, Manchester M15 6PB, England

R. Lewandowski, Marketing Systems GMBH, Postfach 230109, Hunsruckstraße 9a, D‐4300 ESSEN 1 (Bredeney), West Germany

J. Newton and E. Parzen, Institute of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A.

R. Winkler, Indiana University, Graduate School of Business, Bloomington, IN 47405, U.S.A.

Spyros Makridakis is Professor of Management Science at INSEAD, France. He received his degree from the School of Industrial Studies in Greece and his M.B.A. and Ph.D. from New York University. He has published extensively in the areas of general systems and forecasting and has co‐authored Computer‐Aided Modeling for Managers (Addison‐Wesley, 1972), Forecasting Methods for Management, Third Edition (Wiley, 1979), Interactive Forecasting, Second Edition (Holden‐Day, 1978), and Forecasting: Methods and Applications (Wiley‐Hamilton. 1978). He has also been the co‐editor of TIMS Studies, Vol. 12, in Management Science. After his first degree in Statistics. Allan Andersen completed a Ph.D. in the Faculty of Economics at the University of Queensland. At present, he is a lecturer at the University of Sydney in the Department of Statistics. Allan's research interests lie in the general field of forecasting; in particular the time‐series approaches to the subject. He has published more than ten articles, and has co‐authored a book on non‐linear time‐series methods.

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INSEAD, 77305 Fontainebleau, France

A. Andersen, Department of Economic Statistics, The University of Sydney, New South Wales 2006, Australia

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INSEAD, 77305 Fontainebleau, France

Robert Carbone is the chairman of the Department of Management Sciences at La Faculte des Sciences de l'Administration de l'Université Laval. His Ph.D. is in urban and public affairs from Carnegie‐Mellon University. He is a member of AIDS, TIMS, and ORSA. His papers have appeared in The Journal of Environmental Systems, Management Science, IN FOR, Atmospheric Environment, and other journals. His principal current research interest is in data analysis and forecasting.

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INSEAD, 77305 Fontainebleau, France

R. Carbone, Faculté des Sciences de l'Administration, Université Laval, Quebec, Canada G1K 7P4

Robert Fildes is a lecturer in business forecasting at the Manchester Business School, University of Manchester. He received a Bachelor's degree (mathematics) from Oxford and a Ph.D. (statistics) from the University of California. He is co‐author of Forecasting for Business (Longmans, 1976) and an editor of Forecasting and Planning (Teakfield, 1978). He has also authored several articles on forecasting and applied statistics and served as a consultant in these fields. During 1978 he taught at the University of British Columbia and the University of California Berkeley.

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INSEAD, 77305 Fontainebleau, France

Michèle Hibon is currently a research assistant at INSEAD. For the last several years she has been working on various studies dealing with forecasting accuracy of time‐series methods. Before INSEAD, she was associated with the Ecole Polytechnique (Laboratoire de Recherche de Mecanique des Solides) as a computer specialist. She holds a degree in science and a diploma in advanced studies in physics.

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INSEAD, 77305 Fontainebleau, France

Rudolf Lewandowski, born 1938 in Valencia/Spain, studied mathematics and economics at the Sorbonne in Paris, and the universities of Bochum and Bonn and obtained degrees in both subjects. He came to the Federal Republic of Germany in 1962 as a mathematical adviser for a big French company. In 1966 he became the manager of the operations research, economics and marketing department in a leading software firm in West Germany, and founded in 1973‘MARKETING SYSTEMS’, and has been its general manager since. In 1973, he obtained a doctoral degree in economics at the Sorbonne. He has published papers on Markov processes, forecasting methodology and forecast systems, and is author of Prognose und Informationssysteme und ihre Anwendungen, 2 vols. (De Giuyter‐Verlag, Berlin 1974 and 1980), and La Prévision à Court Terme (Dunod, Paris 1979). He has lectured at various European universities, and is a member of several European Marketing Associations.

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INSEAD, 77305 Fontainebleau, France

Joseph Newton is an authority on the theory and algorithms of time series analysis particularly in the area of multivariate time series. His dissertation was written in 1975 at SUNY at Buffalo in the area of estimating the parameters of vector valued autoregressive moving average time series. His publications range from work on the asymptotic distribution of maximum likelihood estimators in multiple time series to the development of efficient algorithms for the prediction of time series. He has been a research assistant professor at SUNY at Buffalo and is currently an assistant professor in the Institute of Statistics at Texas A&M University.

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INSEAD, 77305 Fontainebleau, France

Emanuel Parzen is Distinguished Professor of Statistics at Texas A&M University. He has made research contributions to probability limit theorems, statistical spectral analysis by kernel methods, statistical communication theory, time series analysis by reproducing kernel Hilberg space methods, probability density estimation, multiple time series analysis, statistical spectral analysis by autoregressive methods, order determination criteria for autoregressive schemes, forecasting, and non‐parametric statistical data modelling using quantile and density‐quantile functions. He is the author of two widely used books: Modern Probability Theory and Its Applications (1960) and Stochastic Processes (1962).

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INSEAD, 77305 Fontainebleau, France

Robert Winkler is a distinguished professor of Quantitative Business Analysis in the Graduate School of Business, Indiana University, Bloomington, Indiana. For the 1980‐81 academic year he was a Visiting Professor at INSEAD, Fontainebleau, France. He received a B.S. from the University of Illinois and a Ph.D. from the University of Chicago, and he has held visiting positions at the University of Washington, the International Institute for Applied Systems Analysis, and Stanford University. He is the author of two books and numerous journal articles. His primary research interests involve probability forecasting, Bayesian inference, and statistical decision theory. He is a member of Tl MS and is currently serving as a Departmental Editor for Management Science.

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First published: April/June 1982

https://doi.org/10.1002/for.3980010202

Citations: 812

Abstract

In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.

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Wiley Online Library
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Spyros Makridakis, Fotios Petropoulos, The M4 competition: Conclusions, International Journal of Forecasting, 10.1016/j.ijforecast.2019.05.006, (2019).
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Crossref
Nikolaos P. Bakas, Numerical Solution for the Extrapolation Problem of Analytic Functions, Research, 10.34133/2019/3903187, 2019, (1-10), (2019).
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Crossref
Josef Arlt, Peter Trcka, Automatic SARIMA modeling and forecast accuracy, Communications in Statistics - Simulation and Computation, 10.1080/03610918.2019.1618471, (1-22), (2019).
Crossref
S Ratnasari, undefined Yuniaristanto, R Zakaria, Demand Forecasting with Five Parameter Exponential Smoothing, IOP Conference Series: Materials Science and Engineering, 10.1088/1757-899X/495/1/012014, 495, (012014), (2019).
Crossref
A Nurlatifah, S B Sipayung, B Siswanto, Rainfall Scenario of West Nusa Tenggara in 2040 Based on CCAM RCP 4.5, IOP Conference Series: Earth and Environmental Science, 10.1088/1755-1315/303/1/012033, 303, (012033), (2019).
Crossref
A. S. Kulyasova, A. R. Esina, V. D. Svirchevskiy, Economic and mathematical modeling as an effective tool of the analysis of economic processes in industry, Russian Journal of Industrial Economics, 10.17073/2072-1633-2019-3-316-322, 12, 3, (316-322), (2019).
Crossref
Evangelos Spiliotis, Fotios Petropoulos, Vassilios Assimakopoulos, Improving the forecasting performance of temporal hierarchies, PLOS ONE, 10.1371/journal.pone.0223422, 14, 10, (e0223422), (2019).
Crossref
B. L. Kurilin, V. Y. Kisselevskaya-Babinina, N. A. Karasyov, I. V. Kisselevskaya-Babinina, E. V. Kislukhkina, V. A. Vasilyev, Selection of Prediction Method of Basic Statistical Work Parameters of N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Healthcare Department, Russian Sklifosovsky Journal "Emergency Medical Care", 10.23934/2223-9022-2019-8-3-246-256, 8, 3, (246-256), (2019).
Crossref
Jiří Šindelář, Sales forecasting in financial distribution: a comparison of quantitative forecasting methods, Journal of Financial Services Marketing, 10.1057/s41264-019-00068-3, (2019).
Crossref
Guckan Yapar, Hanife Taylan Selamlar, Sedat Capar, Idil Yavuz, ATA Method, Hacettepe Journal of Mathematics and Statistics, 10.15672/hujms.461032, (1-7), (2019).
Crossref
Vahid Nourani, Gozen Elkiran, Jazuli Abdullahi, Ala Tahsin, Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble, Natural Resources Research, 10.1007/s11053-018-09450-9, (2019).
Crossref
Serkan ARAS, Kripto Para Fiyatlarının Klasik ve Yapay Sinir Ağı Modelleri ile Tahmini, Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10.36543/kauiibfd.2019.026, 10, 20, (608-640), (2019).
Crossref
Sajjad Rahmanzadeh, Mir Saman Pishvaee, Mohammad Reza Rasouli, Integrated innovative product design and supply chain tactical planning within a blockchain platform, International Journal of Production Research, 10.1080/00207543.2019.1651947, (1-21), (2019).
Crossref
Sha Li, Daniel A. Griffith, Hong Shu, Temperature prediction based on a space–time regression-kriging model, Journal of Applied Statistics, 10.1080/02664763.2019.1671962, (1-23), (2019).
Crossref
Ivan Svetunkov, John E. Boylan, State-space ARIMA for supply-chain forecasting, International Journal of Production Research, 10.1080/00207543.2019.1600764, (1-10), (2019).
Crossref
Waddah Waheeb, Rozaida Ghazali, A novel error-output recurrent neural network model for time series forecasting, Neural Computing and Applications, 10.1007/s00521-019-04474-5, (2019).
Crossref
Ji Wu, Xian Cheng, Stephen Shaoyi Liao, Tourism forecast combination using the stochastic frontier analysis technique, Tourism Economics, 10.1177/1354816619868089, (135481661986808), (2019).
Crossref
Zoe Theocharis, Leonard A. Smith, Nigel Harvey, The influence of graphical format on judgmental forecasting accuracy: Lines versus points, FUTURES & FORESIGHT SCIENCE, 10.1002/ffo2.7, 1, 1, (2018).
Wiley Online Library
Qiwei Hu, John E. Boylan, Huijing Chen, Ashraf Labib, OR in spare parts management: A review, European Journal of Operational Research, 10.1016/j.ejor.2017.07.058, 266, 2, (395-414), (2018).
Crossref
Konstantinos Nikolopoulos, Fotios Petropoulos, Forecasting for big data: Does suboptimality matter?, Computers & Operations Research, 10.1016/j.cor.2017.05.007, 98, (322-329), (2018).
Crossref
Fotios Petropoulos, Rob J. Hyndman, Christoph Bergmeir, Exploring the sources of uncertainty: Why does bagging for time series forecasting work?, European Journal of Operational Research, 10.1016/j.ejor.2018.01.045, 268, 2, (545-554), (2018).
Crossref
Spyros Makridakis, Vassilios Assimakopoulos, Evangelos Spiliotis, Objectivity, reproducibility and replicability in forecasting research, International Journal of Forecasting, 10.1016/j.ijforecast.2018.05.001, 34, 4, (835-838), (2018).
Crossref
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, The M4 Competition: Results, findings, conclusion and way forward, International Journal of Forecasting, 10.1016/j.ijforecast.2018.06.001, 34, 4, (802-808), (2018).
Crossref
Ezgi Avci, Wolfgang Ketter, Eric van Heck, Managing electricity price modeling risk via ensemble forecasting: The case of Turkey, Energy Policy, 10.1016/j.enpol.2018.08.053, 123, (390-403), (2018).
Crossref
Dieu.T.T. Do, Dongkyu Lee, Jaehong Lee, Material optimization of functionally graded plates using deep neural network and modified symbiotic organisms search for eigenvalue problems, Composites Part B: Engineering, 10.1016/j.compositesb.2018.09.087, (2018).
Crossref
Korkut Bekiroglu, Okan Duru, Emrah Gulay, Rong Su, Constantino Lagoa, Predictive analytics of crude oil prices by utilizing the intelligent model search engine, Applied Energy, 10.1016/j.apenergy.2018.07.071, 228, (2387-2397), (2018).
Crossref
Zahra Haji Rahimi, Mehdi Khashei, A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting, Computers & Industrial Engineering, 10.1016/j.cie.2018.02.023, 118, (44-53), (2018).
Crossref
Eun-A Sim, Seunghye Lee, Jaehong Lee, Prediction of Static and Dynamic Behavior of Truss Structures Using Deep Learning, Journal of The korean Association For Spatial Structures, 10.9712/KASS.2018.18.4.69, 18, 4, (69-80), (2018).
Crossref
Ali Vaezi, Manish Verma, Railroad transportation of crude oil in Canada: Developing long-term forecasts, and evaluating the impact of proposed pipeline projects, Journal of Transport Geography, 10.1016/j.jtrangeo.2018.04.019, 69, (98-111), (2018).
Crossref
Razvan Stefanescu, Ramona Dumitriu, Introducere n Analiza Trendului, Partea a Patra (Introduction to Trend Analysis, Part 4), SSRN Electronic Journal, 10.2139/ssrn.3148768, (2018).
Crossref
Ruud H. Teunter, M. Zied Babai, Jos A.C. Bokhorst, Aris A. Syntetos, Revisiting the value of information sharing in two-stage supply chains, European Journal of Operational Research, 10.1016/j.ejor.2018.04.040, 270, 3, (1044-1052), (2018).
Crossref
Georg Peters, Richard Weber, dynXcube – Categorizing dynamic data analysis, Information Sciences, 10.1016/j.ins.2018.06.026, 463-464, (21-32), (2018).
Crossref
Salvatore Cannella, Roberto Dominguez, Borja Ponte, Jose M. Framinan, Capacity restrictions and supply chain performance: Modelling and analysing load-dependent lead times, International Journal of Production Economics, 10.1016/j.ijpe.2018.08.008, 204, (264-277), (2018).
Crossref
Alexey Averkin, Sergey Yarushev, Hybrid Neural Networks for Time Series Forecasting, Artificial Intelligence, 10.1007/978-3-030-00617-4_21, (230-239), (2018).
Crossref
Jefferson Cavalcante, Joaquim Celestino Junior, Ahmed Patel, undefined, 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), 10.1109/AINA.2018.00038, (181-188), (2018).
Crossref
Lida Mercedes Barba Maggi, Lida Mercedes Barba Maggi, Times Series Analysis, Multiscale Forecasting Models, 10.1007/978-3-319-94992-5, (1-29), (2018).
Crossref
R M Salleh, N I Zawawi, Z F Gan, M E Nor, Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach, Journal of Physics: Conference Series, 10.1088/1742-6596/995/1/012039, 995, (012039), (2018).
Crossref
Nitin Singh, Saddam Hussain, Shailesh Tiwari, A PSO-Based ANN Model for Short-Term Electricity Price Forecasting, Ambient Communications and Computer Systems, 10.1007/978-981-10-7386-1_47, (553-563), (2018).
Crossref
Han Lin Shang, Steven Haberman, Model confidence sets and forecast combination: an application to age-specific mortality, Genus, 10.1186/s41118-018-0043-9, 74, 1, (2018).
Crossref
Niágara Rodrigues, Luciano Losekann, Getulio Silveira Filho, Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response, Energy Economics, 10.1016/j.eneco.2018.07.005, 74, (644-655), (2018).
Crossref
John D. Levendis, Florian Schütz, Christof Sohn, Non-stationarity and ARIMA(p,d,q) Processes, Erste Hilfe bei Brustkrebs, 10.1007/978-3-319-98282-3_5, (101-122), (2018).
Crossref
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Volume1, Issue2

April/June 1982

Pages 111-153

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