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Forecasting Trends in Time Series

Citations

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Cited by:

  1. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).
  2. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
  3. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
  4. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
  5. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, February.
  6. Nikolaos Kourentzes & Dong Li & Arne K. Strauss, 2019. "Unconstraining methods for revenue management systems under small demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 27-41, February.
  7. Fernández-Amador, Octavio & Francois, Joseph F. & Oberdabernig, Doris A. & Tomberger, Patrick, 2020. "The methane footprint of nations: Stylized facts from a global panel dataset," Ecological Economics, Elsevier, vol. 170(C).
  8. Everette S. Gardner, 1999. "Note: Rule-Based Forecasting vs. Damped-Trend Exponential Smoothing," Management Science, INFORMS, vol. 45(8), pages 1169-1176, August.
  9. Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, vol. 9(12), pages 1-19, December.
  10. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
  11. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
  12. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.
  13. Gardner, Everette S., 2015. "Conservative forecasting with the damped trend," Journal of Business Research, Elsevier, vol. 68(8), pages 1739-1741.
  14. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2019. "Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors," International Journal of Production Economics, Elsevier, vol. 209(C), pages 92-102.
  15. E S Gardner & E McKenzie, 2011. "Why the damped trend works," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1177-1180, June.
  16. 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).
  17. Snyder Ralph D & Forbes Catherine S, 2003. "Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
  18. Masuda, Tadayoshi & Goldsmith, Peter D., 2012. "China's Meat and Egg Production and Soybean Meal Demand for Feed: An Elasticity Analysis and Long-Term Projections," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 15(3), pages 1-20, September.
  19. Fiorucci, Jose A. & Pellegrini, Tiago R. & Louzada, Francisco & Petropoulos, Fotios & Koehler, Anne B., 2016. "Models for optimising the theta method and their relationship to state space models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1151-1161.
  20. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
  21. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
  22. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
  23. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
  24. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
  25. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
  26. Businger, Mark P. & Read, Robert R., 1999. "Identification of demand patterns for selective processing: a case study," Omega, Elsevier, vol. 27(2), pages 189-200, April.
  27. 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.
  28. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
  29. Vokurka, Robert J. & Flores, Benito E. & Pearce, Stephen L., 1996. "Automatic feature identification and graphical support in rule-based forecasting: a comparison," International Journal of Forecasting, Elsevier, vol. 12(4), pages 495-512, December.
  30. Miller, Don M. & Williams, Dan, 2004. "Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program," International Journal of Forecasting, Elsevier, vol. 20(4), pages 529-549.
  31. Park, Jin Suk & Newaz, Mohammad Khaleq, 2021. "Liquidity and short-run predictability: Evidence from international stock markets," Global Finance Journal, Elsevier, vol. 50(C).
  32. Zhen Zeng & Tucker Balch & Manuela Veloso, 2021. "Deep Video Prediction for Time Series Forecasting," Papers 2102.12061, arXiv.org, revised Nov 2021.
  33. Beaumont, Adrian N., 2014. "Data transforms with exponential smoothing methods of forecasting," International Journal of Forecasting, Elsevier, vol. 30(4), pages 918-927.
  34. Ralph D. Snyder & Anne B. Koehler, 2006. "Incorporating a Tracking Signal into State Space Models for Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 16/06, Monash University, Department of Econometrics and Business Statistics.
  35. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
  36. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
  37. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
  38. JS Armstrong, 2004. "Research on Forecasting: A Quarter-Century Review, 1960-1984," General Economics and Teaching 0412006, University Library of Munich, Germany.
  39. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
  40. Javier Arroyo & Rosa Espínola & Carlos Maté, 2011. "Different Approaches to Forecast Interval Time Series: A Comparison in Finance," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 169-191, February.
  41. Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
  42. Fildes, Robert & Hibon, Michele & Makridakis, Spyros & Meade, Nigel, 1998. "Generalising about univariate forecasting methods: further empirical evidence," International Journal of Forecasting, Elsevier, vol. 14(3), pages 339-358, September.
  43. J. S. Armstrong & R. Brodie & S. McIntyre, 2005. "Forecasting Methods for Marketing:* Review of Empirical Research," General Economics and Teaching 0502023, University Library of Munich, Germany.
  44. Armstrong, J. Scott & Morwitz, Vicki G. & Kumar, V., 2000. "Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 383-397.
  45. Lim, Joa Sang & O'Connor, Marcus, 1996. "Judgmental forecasting with time series and causal information," International Journal of Forecasting, Elsevier, vol. 12(1), pages 139-153, March.
  46. Bahman Rostami‐Tabar & M. Zied Babai & Aris Syntetos & Yves Ducq, 2013. "Demand forecasting by temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(6), pages 479-498, September.
  47. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
  48. Ivanovski, Zoran & Milenkovski, Ace & Narasanov, Zoran, 2018. "Time Series Forecasting Using A Moving Average Model For Extrapolation Of Number Of Tourist," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(2), pages 121-132.
  49. repec:jss:jstsof:27:i03 is not listed on IDEAS
  50. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
  51. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  52. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
  53. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
  54. Milad Delavary Foroutaghe & Abolfazl Mohammadzadeh Moghaddam & Vahid Fakoor, 2019. "Time trends in gender-specific incidence rates of road traffic injuries in Iran," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.
  55. Rossetti Renato, 2019. "Forecasting the Sales of Console Games for the Italian Market," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 76-88, September.
  56. Acar, Yavuz & Gardner, Everette S., 2012. "Forecasting method selection in a global supply chain," International Journal of Forecasting, Elsevier, vol. 28(4), pages 842-848.
  57. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
  58. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
  59. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
  60. Ralph D. Snyder & Anne B. Koehler, 2008. "A View of Damped Trend as Incorporating a Tracking Signal into a State Space Model," Monash Econometrics and Business Statistics Working Papers 7/08, Monash University, Department of Econometrics and Business Statistics.
  61. Samagaio, António & Wolters, Mark, 2010. "Comparative analysis of government forecasts for the Lisbon Airport," Journal of Air Transport Management, Elsevier, vol. 16(4), pages 213-217.
  62. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
  63. Thomson, Mary E. & Pollock, Andrew C. & Gönül, M. Sinan & Önkal, Dilek, 2013. "Effects of trend strength and direction on performance and consistency in judgmental exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 337-353.
  64. J D Bermúdez & J V Segura & E Vercher, 2006. "Improving demand forecasting accuracy using nonlinear programming software," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 94-100, January.
  65. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
  66. Armstrong, J. Scott, 2004. "Damped seasonality factors: Introduction," International Journal of Forecasting, Elsevier, vol. 20(4), pages 525-527.
  67. José V. Segura-Heras & José D. Bermúdez & Ana Corberán-Vallet & Enriqueta Vercher, 2022. "Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts," Mathematics, MDPI, vol. 10(5), pages 1-12, February.
  68. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
  69. Eren Bas & Erol Egrioglu & Ufuk Yolcu, 2021. "Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm," Forecasting, MDPI, vol. 3(4), pages 1-11, November.
  70. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.
  71. Avgustin Milanov, 2020. "Forecasting Of Some Key Indicators Of The Rfi And Rfp Processes Of The Bulgarian Mobile Telecommunication Operators," Economics & Law, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 2(2), pages 62-70.
  72. Timothy M. Young & Ampalavanar Nanthakumar & Hari Nanthakumar, 2021. "On the Use of Copula for Quality Control Based on an AR(1) Model," Mathematics, MDPI, vol. 9(18), pages 1-13, September.
  73. Bahman Rostami‐Tabar & Mohamed Zied Babai & Aris Syntetos & Yves Ducq, 2014. "A note on the forecast performance of temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(7), pages 489-500, October.
  74. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
  75. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
  76. Dimitrov, Preslav & Daleva, Diana & Stoyanova, Milena, 2017. "Forecasting of the Volume of the SPA and Wellness Tourism Receipts in the South-West Bulgaria," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 5(2), pages 83-99.
  77. Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
  78. Lingbing Feng & Yanlin Shi, 2018. "Forecasting mortality rates: multivariate or univariate models?," Journal of Population Research, Springer, vol. 35(3), pages 289-318, September.
  79. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
  80. Stefanescu, Răzvan & Dumitriu, Ramona, 2019. "Obiective ale analizei trendurilor seriilor de timp discrete [Objectives of the analysis of trends in discrete time series]," MPRA Paper 97821, University Library of Munich, Germany, revised 23 Dec 2019.
  81. McKenzie, Eddie & Gardner Jr., Everette S., 2010. "Damped trend exponential smoothing: A modelling viewpoint," International Journal of Forecasting, Elsevier, vol. 26(4), pages 661-665, October.
  82. Gardner Jr., Everette S. & Diaz-Saiz, Joaquin, 2008. "Exponential smoothing in the telecommunications data," International Journal of Forecasting, Elsevier, vol. 24(1), pages 170-174.
  83. Grubb, Howard & Mason, Alexina, 2001. "Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend," International Journal of Forecasting, Elsevier, vol. 17(1), pages 71-82.
  84. Godfrey, Gregory A. & Powell, Warren B., 2000. "Adaptive estimation of daily demands with complex calendar effects for freight transportation," Transportation Research Part B: Methodological, Elsevier, vol. 34(6), pages 451-469, August.
  85. Gardner, Everette S. & Anderson, Elizabeth A., 1997. "Focus forecasting reconsidered," International Journal of Forecasting, Elsevier, vol. 13(4), pages 501-508, December.
  86. Tashman, Leonard J. & Kruk, Joshua M., 1996. "The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 12(2), pages 235-253, June.
  87. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
  88. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
  89. Reimers, Stian & Harvey, Nigel, 2011. "Sensitivity to autocorrelation in judgmental time series forecasting," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1196-1214, October.
  90. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
  91. Warburton, Roger D.H. & Hodgson, J.P.E. & Nielsen, E.H., 2014. "Exact solutions to the supply chain equations for arbitrary, time-dependent demands," International Journal of Production Economics, Elsevier, vol. 151(C), pages 195-205.
  92. Yanlin Shi & Sixian Tang & Jackie Li, 2020. "A Two-Population Extension of the Exponential Smoothing State Space Model with a Smoothing Penalisation Scheme," Risks, MDPI, vol. 8(3), pages 1-18, June.
  93. Giacomo Sbrana, 2010. "Forecasting damped trend exponential smoothing: an algebraic viewpoint," Working Papers 10-08, Association Française de Cliométrie (AFC).
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