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Accuracy measures: theoretical and practical concerns

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Cited by:

  1. Yoon, Sung Wook & Jeong, Suk Jae, 2015. "An alternative methodology for planning baggage carousel capacity expansion: A case study of Incheon International Airport," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 63-74.
  2. repec:lan:wpaper:3046 is not listed on IDEAS
  3. Mouatadid, Soukayna & Adamowski, Jan F. & Tiwari, Mukesh K. & Quilty, John M., 2019. "Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting," Agricultural Water Management, Elsevier, vol. 219(C), pages 72-85.
  4. Louie Ren & Yong Glasure, 2009. "Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(4), pages 409-420, November.
  5. Lago, Jesus & De Ridder, Fjo & Vrancx, Peter & De Schutter, Bart, 2018. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, Elsevier, vol. 211(C), pages 890-903.
  6. Miriam Steurer & Robert Hill, 2019. "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers 2019-02, University of Graz, Department of Economics.
  7. Jose, Victor Richmond R. & Winkler, Robert L., 2008. "Simple robust averages of forecasts: Some empirical results," International Journal of Forecasting, Elsevier, vol. 24(1), pages 163-169.
  8. Jacinta Chan Phooi M’ng & Mohammadali Mehralizadeh, 2016. "Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
  9. Yi-Chung Hu, 2021. "Forecasting tourism demand using fractional grey prediction models with Fourier series," Annals of Operations Research, Springer, vol. 300(2), pages 467-491, May.
  10. 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).
  11. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
  12. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
  13. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
  14. Yi-Chung Hu, 2021. "Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 315-331, February.
  15. repec:lan:wpaper:3324 is not listed on IDEAS
  16. 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.
  17. Alexander Burck & Martin Glaum & Kati Schnürer, 2018. "Cash-Flow-Planung – Anforderungen und praktische Umsetzung im internationalen Konzern [Cash-Flow Planning – Requirements and Implementation in a Multinational Corporation]," Schmalenbach Journal of Business Research, Springer, vol. 70(4), pages 393-425, December.
  18. Colin Singleton & Peter Grindrod, 2021. "Forecasting for Battery Storage: Choosing the Error Metric," Energies, MDPI, vol. 14(19), pages 1-11, October.
  19. 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.
  20. Che-Jung Chang & Guiping Li & Shao-Qing Zhang & Kun-Peng Yu, 2019. "Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions," IJERPH, MDPI, vol. 16(14), pages 1-10, July.
  21. Peng Jiang & Yi-Chung Hu & Wenbao Wang & Hang Jiang & Geng Wu, 2020. "Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting," Mathematics, MDPI, vol. 8(6), pages 1-12, June.
  22. 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.
  23. Zhengwei Huang & Jin Huang & Jintao Min, 2022. "SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching," Energies, MDPI, vol. 15(20), pages 1-16, October.
  24. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
  25. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
  26. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
  27. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
  28. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
  29. Hao, Meiling & Lin, Yunyuan & Zhao, Xingqiu, 2016. "A relative error-based approach for variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 250-262.
  30. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
  31. Christos J. Negakis, 2005. "Are Earnings More Informative than Residual Income in Valuation Models?," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 45-58.
  32. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
  33. Andreas Marcus Gohs, 2022. "Forecasting Market Diffusion of Innovative Battery-Electric and Conventional Vehicles in Germany under Model Uncertainty," MAGKS Papers on Economics 202209, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  34. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  35. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
  36. Lawrence, Michael & O'Connor, Marcus & Edmundson, Bob, 2000. "A field study of sales forecasting accuracy and processes," European Journal of Operational Research, Elsevier, vol. 122(1), pages 151-160, April.
  37. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Reis, Agnaldo J.R. & Enayatifar, Rasul & Souza, Marcone J.F. & Guimarães, Frederico G., 2016. "A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment," Applied Energy, Elsevier, vol. 169(C), pages 567-584.
  38. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
  39. Albert Veenstra & Rogier Harmelink, 2021. "On the quality of ship arrival predictions," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(4), pages 655-673, December.
  40. Diamantopoulos, Adamantios & Winklhofer, Heidi, 2003. "Export sales forecasting by UK firms: Technique utilization and impact on forecast accuracy," Journal of Business Research, Elsevier, vol. 56(1), pages 45-54, January.
  41. Chen, Shu-Heng & Yeh, Chia-Hsuan, 1997. "Toward a computable approach to the efficient market hypothesis: An application of genetic programming," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 1043-1063, June.
  42. Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
  43. Yi-Chung Hu & Peng Jiang, 2017. "Forecasting energy demand using neural-network-based grey residual modification models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 556-565, May.
  44. Pär Stockhammar & Lars-Erik Öller, 2011. "On the probability distribution of economic growth," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 2023-2041, November.
  45. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
  46. Halužan, Marko & Verbič, Miroslav & Zorić, Jelena, 2020. "Performance of alternative electricity price forecasting methods: Findings from the Greek and Hungarian power exchanges," Applied Energy, Elsevier, vol. 277(C).
  47. repec:kap:iaecre:v:15:y:2009:i:4:p:409-420 is not listed on IDEAS
  48. Xiaohan Xu & Roy Anthony Rogers & Mario Arturo Ruiz Estrada, 2023. "A Novel Prediction Model: ELM-ABC for Annual GDP in the Case of SCO Countries," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1545-1566, December.
  49. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
  50. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
  51. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
  52. Yi-Chung Hu & Peng Jiang & Jung-Fa Tsai & Ching-Ying Yu, 2021. "An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
  53. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
  54. Dominik Martin & Philipp Spitzer & Niklas Kuhl, 2020. "A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs," Papers 2004.10537, arXiv.org.
  55. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
  56. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
  57. Timothy Webb, 2022. "Forecasting at capacity: the bias of unconstrained forecasts in model evaluation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 645-656, December.
  58. Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
  59. Ciaran O'Connor & Joseph Collins & Steven Prestwich & Andrea Visentin, 2024. "Electricity Price Forecasting in the Irish Balancing Market," Papers 2402.06714, arXiv.org.
  60. Debabrata Mukhopadhyay & Nityananda Sarkar, 2013. "Stock Returns Under Alternative Volatility and Distributional Assumptions: The Case for India," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 1-19, April.
  61. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
  62. Balazs Pager & Zsuzsanna Zsibókb, 2020. "Regionalizing National-Level Growth Projections in the Visegrad Countries – The Issue Of Ex-Post Rescaling," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 14(1), pages 1-24, JUNE.
  63. Lawrence, Michael & O'Connor, Marcus, 2000. "Sales forecasting updates: how good are they in practice?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 369-382.
  64. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
  65. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
  66. Davis, Donna F. & Mentzer, John T., 2007. "Organizational factors in sales forecasting management," International Journal of Forecasting, Elsevier, vol. 23(3), pages 475-495.
  67. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated fact," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
  68. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
  69. Diamantopoulos, Adamantios & Winklhofer, Heidi, 1999. "The impact of firm and export characteristics on the accuracy of export sales forecasts: evidence from UK exporters," International Journal of Forecasting, Elsevier, vol. 15(1), pages 67-81, February.
  70. Luca Di Persio & Nicola Fraccarolo, 2023. "Energy Consumption Forecasts by Gradient Boosting Regression Trees," Mathematics, MDPI, vol. 11(5), pages 1-17, February.
  71. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
  72. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
  73. Schlueter, Stephan, 2010. "A long-term/short-term model for daily electricity prices with dynamic volatility," Energy Economics, Elsevier, vol. 32(5), pages 1074-1081, September.
  74. Nik Dawson & Sacha Molitorisz & Marian-Andrei Rizoiu & Peter Fray, 2020. "Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020," Papers 2008.12459, arXiv.org, revised Feb 2021.
  75. Zhuo Chen & Seong-Hoon Cho & Neelam Poudyal & Roland K. Roberts, 2009. "Forecasting Housing Prices under Different Market Segmentation Assumptions," Urban Studies, Urban Studies Journal Limited, vol. 46(1), pages 167-187, January.
  76. Jiayue Xu, 2022. "A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market," Papers 2201.13235, arXiv.org.
  77. Katleho Makatjane & Ntebogang Moroke, 2021. "Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index," IJFS, MDPI, vol. 9(2), pages 1-18, March.
  78. Christian Ganser & Marc Keuschnigg, 2018. "Social Influence Strengthens Crowd Wisdom Under Voting," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-23, September.
  79. repec:lan:wpaper:592830 is not listed on IDEAS
  80. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
  81. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
  82. Heng-Li Yang & Han-Chou Lin, 2017. "Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 99-116, January.
  83. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
  84. Luca Berchicci & Murat Tarakci, 2022. "Aspiration formation and attention rules," Strategic Management Journal, Wiley Blackwell, vol. 43(8), pages 1575-1601, August.
  85. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
  86. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
  87. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
  88. repec:aaa:journl:v:3:y:1999:i:1:p:87-100 is not listed on IDEAS
  89. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
  90. Schmidt, Johannes & Leduc, Sylvain & Dotzauer, Erik & Kindermann, Georg & Schmid, Erwin, 2010. "Cost-effective CO2 emission reduction through heat, power and biofuel production from woody biomass: A spatially explicit comparison of conversion technologies," Applied Energy, Elsevier, vol. 87(7), pages 2128-2141, July.
  91. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1058-1065, October.
  92. Sun Sun & Nan Luo & Erik Stenberg & Lars Lindholm & Klas-Göran Sahlén & Karl A. Franklin & Yang Cao, 2022. "Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery," IJERPH, MDPI, vol. 19(17), pages 1-16, August.
  93. En-Chih Chang, 2018. "Improving Performance for Full-Bridge Inverter of Wind Energy Conversion System Using a Fast and Efficient Control Technique," Energies, MDPI, vol. 11(2), pages 1-16, January.
  94. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
  95. Mahmood Ul Hassan & Pär Stockhammar, 2016. "Fitting probability distributions to economic growth: a maximum likelihood approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1583-1603, July.
  96. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
  97. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
  98. O'Connor, Marcus & Remus, William & Griggs, Kenneth, 2000. "Does updating judgmental forecasts improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 101-109.
  99. Maria Tzitiridou-Chatzopoulou & Georgia Zournatzidou & Michael Kourakos, 2024. "Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland," IJERPH, MDPI, vol. 21(7), pages 1-13, June.
  100. Dean W. Wichern & Benito E. Flores, 2005. "Evaluating forecasts: a look at aggregate bias and accuracy measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 433-451.
  101. Che-Yu Hung & Chien-Chih Wang & Shi-Woei Lin & Bernard C. Jiang, 2022. "An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
  102. 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.
  103. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
  104. Bruce G. S. Hardie & Peter S. Fader & Robert Zeithammer, 2003. "Forecasting new product trial in a controlled test market environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 391-410.
  105. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
  106. Son, Hyojoo & Kim, Changwan, 2017. "Short-term forecasting of electricity demand for the residential sector using weather and social variables," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 200-207.
  107. Remus, William & O'Connor, Marcus & Griggs, Kenneth, 1995. "Does reliable information improve the accuracy of judgmental forecasts?," International Journal of Forecasting, Elsevier, vol. 11(2), pages 285-293, June.
  108. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
  109. McKenzie, Jordi, 2011. "Mean absolute percentage error and bias in economic forecasting," Economics Letters, Elsevier, vol. 113(3), pages 259-262.
  110. Jennifer L. Castle & Jurgen A. Doornik & David Hendry, 2019. "Some forecasting principles from the M4 competition," Economics Papers 2019-W01, Economics Group, Nuffield College, University of Oxford.
  111. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
  112. Giacomo Sbrana & Andrea Silvestrini, 2024. "The structural Theta method and its predictive performance in the M4-Competition," Temi di discussione (Economic working papers) 1457, Bank of Italy, Economic Research and International Relations Area.
  113. Alfonso Dufour & Robert F Engle, 2000. "The ACD Model: Predictability of the Time Between Concecutive Trades," ICMA Centre Discussion Papers in Finance icma-dp2000-05, Henley Business School, University of Reading.
  114. Bunn, Derek W. & Taylor, James W., 2001. "Setting accuracy targets for short-term judgemental sales forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 159-169.
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