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A new accuracy measure based on bounded relative error for time series forecasting

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  1. Roman Tkachenko & Ivan Izonin & Pavlo Vitynskyi & Nataliia Lotoshynska & Olena Pavlyuk, 2018. "Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs," Data, MDPI, vol. 3(4), pages 1-14, October.
  2. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
  3. Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019. "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, vol. 84(C).
  4. Brummelhuis, Raymond & Luo, Zhongmin, 2019. "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper 94779, University Library of Munich, Germany.
  5. Wen-Jie Liu & Yu-Ting Bai & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong, 2022. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
  6. 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.
  7. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
  8. Jarimi, Hasila & Al-Waeli, Ali H.A. & Razak, Tajul Rosli & Abu Bakar, Mohd Nazari & Fazlizan, Ahmad & Ibrahim, Adnan & Sopian, Kamaruzzaman, 2022. "Neural network modelling and performance estimation of dual-fluid photovoltaic thermal solar collectors in tropical climate conditions," Renewable Energy, Elsevier, vol. 197(C), pages 1009-1019.
  9. Chaitanya B. Pande & Nadhir Al-Ansari & N. L. Kushwaha & Aman Srivastava & Rabeea Noor & Manish Kumar & Kanak N. Moharir & Ahmed Elbeltagi, 2022. "Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree," Land, MDPI, vol. 11(11), pages 1-24, November.
  10. Aydın, Emre & Turan, Onder, 2023. "Performance models of passenger aircraft and propulsion systems based on particle swarm and Spotted Hyena Optimization methods," Energy, Elsevier, vol. 268(C).
  11. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
  12. Claudia Furlan & Cinzia Mortarino & Mohammad Salim Zahangir, 2021. "Interaction among three substitute products: an extended innovation diffusion model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 269-293, March.
  13. Arash Sioofy Khoojine & Mahboubeh Shadabfar & Yousef Edrisi Tabriz, 2022. "A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
  14. Willams B. F. da Silva & Pedro M. Almeida‐Junior & Abraão D. C. Nascimento, 2023. "Generalized gamma ARMA process for synthetic aperture radar amplitude and intensity data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
  15. Cheng-Hong Yang & Po-Yin Chang, 2020. "Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
  16. 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.
  17. Abraão D. C. Nascimento & Maria C. S. Lima & Hassan Bakouch & Najla Qarmalah, 2023. "Scaled Muth–ARMA Process Applied to Finance Market," Mathematics, MDPI, vol. 11(8), pages 1-18, April.
  18. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
  19. Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
  20. Ly, Kim Tien, 2021. "A COVID-19 forecasting system using adaptive neuro-fuzzy inference," Finance Research Letters, Elsevier, vol. 41(C).
  21. Jenny Cifuentes & Geovanny Marulanda & Antonio Bello & Javier Reneses, 2020. "Air Temperature Forecasting Using Machine Learning Techniques: A Review," Energies, MDPI, vol. 13(16), pages 1-28, August.
  22. Hongxing Li & Charlotte D. Smith & Li Wang & Zheng Li & Chuanlong Xiong & Rong Zhang, 2019. "Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data," IJERPH, MDPI, vol. 16(3), pages 1-9, January.
  23. Nirmala, N. & Dawn, S.S. & Harindra, C., 2020. "Analysis of performance and emission characteristics of Waste cooking oil and Chlorella variabilis MK039712.1 biodiesel blends in a single cylinder, four strokes diesel engine," Renewable Energy, Elsevier, vol. 147(P1), pages 284-292.
  24. Jacobs, Bas & Tobi, Hilde & Hengeveld, Geerten M., 2024. "Linking error measures to model questions," Ecological Modelling, Elsevier, vol. 487(C).
  25. Hend Alrasheed & Alhanoof Althnian & Heba Kurdi & Heila Al-Mgren & Sulaiman Alharbi, 2020. "COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis," IJERPH, MDPI, vol. 17(21), pages 1-24, October.
  26. Haydar Demirhan, 2020. "dLagM: An R package for distributed lag models and ARDL bounds testing," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-23, February.
  27. 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.
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