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25 years of time series forecasting

Citations

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Cited by:

  1. Zhineng Hu & Jing Ma & Liangwei Yang & Xiaoping Li & Meng Pang, 2019. "Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand," Sustainability, MDPI, vol. 11(5), pages 1-25, February.
  2. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
  3. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901.
  4. Salas-Molina, Francisco & Martin, Francisco J. & Rodríguez-Aguilar, Juan A. & Serrà, Joan & Arcos, Josep Ll., 2017. "Empowering cash managers to achieve cost savings by improving predictive accuracy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 403-415.
  5. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
  6. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
  7. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
  8. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
  9. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
  10. Odin Foldvik Eikeland & Filippo Maria Bianchi & Harry Apostoleris & Morten Hansen & Yu-Cheng Chiou & Matteo Chiesa, 2021. "Predicting Energy Demand in Semi-Remote Arctic Locations," Energies, MDPI, vol. 14(4), pages 1-17, February.
  11. Zi‐Yi Guo, 2021. "Out‐of‐sample performance of bias‐corrected estimators for diffusion processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 243-268, March.
  12. Francisco Salas-Molina & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Montserrat Guillen & Francisco J. Martin, 2016. "Empirical analysis of daily cash flow time series and its implications for forecasting," Papers 1611.04941, arXiv.org, revised Jun 2017.
  13. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
  14. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
  15. Singh Abhishek & Mishra G. C., 2015. "Application of Box-Jenkins Method and Artificial Neural Network Procedure for Time Series Forecasting of Prices," Statistics in Transition New Series, Statistics Poland, vol. 16(1), pages 83-96, March.
  16. Jackson, Ilya & Ivanov, Dmitry, 2023. "A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting in the beauty care industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
  17. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
  18. Niu, Linlin & Xu, Xiu & Chen, Ying, 2017. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," Economic Modelling, Elsevier, vol. 66(C), pages 201-213.
  19. Chandadevi Giri & Yan Chen, 2022. "Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry," Forecasting, MDPI, vol. 4(2), pages 1-17, June.
  20. Mingwen Yang & Zhiqiang (Eric) Zheng & Vijay Mookerjee, 2019. "Prescribing Response Strategies to Manage Customer Opinions: A Stochastic Differential Equation Approach," Information Systems Research, INFORMS, vol. 30(2), pages 351-374, June.
  21. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
  22. Toni Toharudin & Resa Septiani Pontoh & Rezzy Eko Caraka & Solichatus Zahroh & Panji Kendogo & Novika Sijabat & Mentari Dara Puspita Sari & Prana Ugiana Gio & Mohammad Basyuni & Bens Pardamean, 2021. "National Vaccination and Local Intervention Impacts on COVID-19 Cases," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
  23. André Souza Oliveira & Raphael Oliveira dos Santos & Bruno Caetano dos Santos Silva & Lilian Lefol Nani Guarieiro & Matthias Angerhausen & Uwe Reisgen & Renelson Ribeiro Sampaio & Bruna Aparecida Souz, 2021. "A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes," Sustainability, MDPI, vol. 13(7), pages 1-23, March.
  24. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
  25. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Fumitaka Furuoka & Luis A. Gil‐Alana, 2021. "A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 960-981, August.
  26. Peroni Chiara, 2009. "A Non-Parametric Investigation of Risk Premia," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(4), pages 1-52, September.
  27. Boylan, John E. & Goodwin, Paul & Mohammadipour, Maryam & Syntetos, Aris A., 2015. "Reproducibility in forecasting research," International Journal of Forecasting, Elsevier, vol. 31(1), pages 79-90.
  28. Nikafkar, Nasrin & Alroaia, Younos Vakil & Heydariyeh, Seyyed Abdollah & Schleiss, Anton J., 2023. "Economic and commercial analysis of reusing dam reservoir sediments," Ecological Economics, Elsevier, vol. 204(PB).
  29. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
  30. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
  31. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
  32. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  33. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
  34. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2008. "How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37620, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  35. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
  36. Man Li & Tao Ye & Peijun Shi & Jian Fang, 2015. "Impacts of the global economic crisis and Tohoku earthquake on Sino–Japan trade: a comparative perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 541-556, January.
  37. Dursun Aydin & Ersin Yilmaz, 2021. "Censored Nonparametric Time-Series Analysis with Autoregressive Error Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 169-202, August.
  38. Peter Nielsen & Liping Jiang & Niels Gorm Malý Rytter & Gang Chen, 2014. "An investigation of forecast horizon and observation fit's influence on an econometric rate forecast model in the liner shipping industry," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(7), pages 667-682, December.
  39. Osman Gulseven, 2020. "Turn-of-the Year Affect in Gold Prices: Decomposition Analysis," Papers 2003.11027, arXiv.org.
  40. Caglar, Abdullah Emre & Daştan, Muhammet & Avci, Salih Bortecine, 2024. "Persistence of disaggregate energy RD&D expenditures in top-five economies: Evidence from artificial neural network approach," Applied Energy, Elsevier, vol. 365(C).
  41. Montgomery, Jacob M. & Hollenbach, Florian M. & Ward, Michael D., 2015. "Calibrating ensemble forecasting models with sparse data in the social sciences," International Journal of Forecasting, Elsevier, vol. 31(3), pages 930-942.
  42. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  43. Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.
  44. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
  45. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
  46. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
  47. Amélie Charles & Olivier Darné & Jae H. Kim, 2016. "Stock Return Predictability: Evaluation based on prediction intervals," Working Papers hal-01295037, HAL.
  48. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
  49. Riezebos, Jan & Zhu, Stuart X., 2020. "Inventory control with seasonality of lead times," Omega, Elsevier, vol. 92(C).
  50. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2013. "Property Market Modelling and Forecasting: A Case for Simplicity," ERES eres2013_10, European Real Estate Society (ERES).
  51. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2012. "Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation," Energy, Elsevier, vol. 39(1), pages 341-355.
  52. Syed Ali Asad Rizvi & Stephen J. Roberts & Michael A. Osborne & Favour Nyikosa, 2017. "A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes," Papers 1705.00891, arXiv.org.
  53. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
  54. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.
  55. Croux, C. & Gelper, S. & Mahieu, K., 2010. "Robust Control Charts for Time Series Data," Discussion Paper 2010-107, Tilburg University, Center for Economic Research.
  56. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
  57. Merten, Michael & Rücker, Fabian & Schoeneberger, Ilka & Sauer, Dirk Uwe, 2020. "Automatic frequency restoration reserve market prediction: Methodology and comparison of various approaches," Applied Energy, Elsevier, vol. 268(C).
  58. Souhaib Ben Taieb & Rob J Hyndman, 2014. "Boosting multi-step autoregressive forecasts," Monash Econometrics and Business Statistics Working Papers 13/14, Monash University, Department of Econometrics and Business Statistics.
  59. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
  60. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
  61. Mirzaei, Danesh & Behbahaninia, Ali & Abdalisousan, Ashkan & Miri Lavasani, Seyed Mohammadreza, 2023. "A novel approach to repair time prediction and availability assessment of the equipment in power generation systems using fuzzy logic and Monte Carlo simulation," Energy, Elsevier, vol. 282(C).
  62. Melchior, Cristiane & Zanini, Roselaine Ruviaro & Guerra, Renata Rojas & Rockenbach, Dinei A., 2021. "Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches," International Journal of Forecasting, Elsevier, vol. 37(2), pages 825-837.
  63. Yam Wing Siu, 2018. "Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-48, June.
  64. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2013. "Hybrid methodology for hourly global radiation forecasting in Mediterranean area," Renewable Energy, Elsevier, vol. 53(C), pages 1-11.
  65. Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
  66. David Harris & Gael M. Martin & Indeewara Perera & Don S. Poskitt, 2017. "Construction and visualization of optimal confidence sets for frequentist distributional forecasts," Monash Econometrics and Business Statistics Working Papers 9/17, Monash University, Department of Econometrics and Business Statistics.
  67. Ferencek Aljaž & Kofjač Davorin & Škraba Andrej & Sašek Blaž & Borštnar Mirjana Kljajić, 2020. "Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period," Business Systems Research, Sciendo, vol. 11(2), pages 36-50, October.
  68. Wu, Ji & Chan, Chee Keong & Zhang, Yu & Xiong, Bin Yu & Zhang, Qing Hai, 2014. "Prediction of solar radiation with genetic approach combing multi-model framework," Renewable Energy, Elsevier, vol. 66(C), pages 132-139.
  69. Guangxun Jin & Ohbyung Kwon, 2021. "Impact of chart image characteristics on stock price prediction with a convolutional neural network," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-18, June.
  70. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
  71. Hui Wang & Jianbo Sun & Weijun Wang, 2018. "Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model," Sustainability, MDPI, vol. 10(8), pages 1-11, July.
  72. Lili Li & Jun Yang & Xin Zou, 2016. "A study of credit risk of Chinese listed companies: ZPP versus KMV," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2697-2710, June.
  73. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
  74. Dale Roberts & Laura Ryan, 2015. "Evidence of speculation in world oil prices," Australian Journal of Management, Australian School of Business, vol. 40(4), pages 630-651, November.
  75. Croux, C. & Gelper, S. & Mahieu, K., 2010. "Robust Control Charts for Time Series Data," Other publications TiSEM 229a21da-3d8a-4764-9d78-5, Tilburg University, School of Economics and Management.
  76. Nazim Choudhury, 2024. "Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks," Mathematics, MDPI, vol. 12(2), pages 1-24, January.
  77. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  78. Jean-Laurent Duchaud & Cyril Voyant & Alexis Fouilloy & Gilles Notton & Marie-Laure Nivet, 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control," Energies, MDPI, vol. 13(14), pages 1-16, July.
  79. Pokorný, Jiří & Froněk, Pavel, 2021. "Price Forecasting Accuracy of the OECD-FAO's Agricultural Outlook and the European Commission DG AGRI's Medium-Term Agricultural Outlook Report," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 13(3), September.
  80. Daniel Ramos & Pedro Faria & Zita Vale & João Mourinho & Regina Correia, 2020. "Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning," Energies, MDPI, vol. 13(18), pages 1-18, September.
  81. Guidi, Francesco, 2010. "Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models," MPRA Paper 19851, University Library of Munich, Germany.
  82. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
  83. Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
  84. Tang, Hui-Wen Vivian & Yin, Mu-Shang, 2012. "Forecasting performance of grey prediction for education expenditure and school enrollment," Economics of Education Review, Elsevier, vol. 31(4), pages 452-462.
  85. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
  86. Martin Madera & Dusan Marcek, 2023. "Intelligence in Finance and Economics for Predicting High-Frequency Data," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
  87. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
  88. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
  89. Goodwin, Paul & Önkal, Dilek & Thomson, Mary, 2010. "Do forecasts expressed as prediction intervals improve production planning decisions?," European Journal of Operational Research, Elsevier, vol. 205(1), pages 195-201, August.
  90. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
  91. Shagun Srivastava & Madhvendra Misra, 2014. "Developing Evaluation Matrix for Critical Success Factors in Technology Forecasting," Global Business Review, International Management Institute, vol. 15(2), pages 363-380, June.
  92. Ling He & Chenyi Hu, 2009. "Impacts of Interval Computing on Stock Market Variability Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 33(3), pages 263-276, April.
  93. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
  94. Lixin Tian & Huan Chen & Zaili Zhen, 2018. "Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
  95. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
  96. repec:bof:bofitp:urn:nbn:fi:bof-201504131155 is not listed on IDEAS
  97. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
  98. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
  99. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.
  100. repec:zbw:bofitp:urn:nbn:fi:bof-201504131155 is not listed on IDEAS
  101. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Paul Doukhan & Gabriel Lang & Anne Leucht & Michael H. Neumann, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 290-314, May.
  102. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
  103. Teddy, S.D. & Ng, S.K., 2011. "Forecasting ATM cash demands using a local learning model of cerebellar associative memory network," International Journal of Forecasting, Elsevier, vol. 27(3), pages 760-776, July.
  104. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
  105. Jia Xu & Longbing Cao, 2023. "Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling," Papers 2305.08778, arXiv.org.
  106. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
  107. Fan-Osuala, Onochie & Zantedeschi, Daniel & Jank, Wolfgang, 2018. "Using past contribution patterns to forecast fundraising outcomes in crowdfunding," International Journal of Forecasting, Elsevier, vol. 34(1), pages 30-44.
  108. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.
  109. Majid Delavari & Nadiya Gandali Alikhani & Esmaeil Naderi, 2013. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 466-475.
  110. Ralf Barkemeyer & Philippe Givry & Frank Figge, 2018. "Trends and patterns in sustainability-related media coverage: A classification of issue-level attention," Environment and Planning C, , vol. 36(5), pages 937-962, August.
  111. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Sultan Al-Yahyai & Mostafa Bakhtvar, 2019. "An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems," Energies, MDPI, vol. 12(22), pages 1-20, November.
  112. Thiago Christiano Silva & Paulo Victor Berri Wilhelm & Diego Raphael Amancio, 2024. "Machine Learning and Economic Forecasting: the role of international trade networks," Working Papers Series 597, Central Bank of Brazil, Research Department.
  113. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
  114. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
  115. Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2920-2933, December.
  116. repec:hum:wpaper:sfb649dp2015-023 is not listed on IDEAS
  117. Hofmeister, Markus & Mosbach, Sebastian & Hammacher, Jörg & Blum, Martin & Röhrig, Gerd & Dörr, Christoph & Flegel, Volker & Bhave, Amit & Kraft, Markus, 2022. "Resource-optimised generation dispatch strategy for district heating systems using dynamic hierarchical optimisation," Applied Energy, Elsevier, vol. 305(C).
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