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A hybrid ARIMA and support vector machines model in stock price forecasting

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  1. Dat Thanh Tran & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Tensor Representation in High-Frequency Financial Data for Price Change Prediction," Papers 1709.01268, arXiv.org, revised Nov 2017.
  2. Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).
  3. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
  4. Tongxiang Liu & Yu Jin & Yuyang Gao, 2019. "A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization," Energies, MDPI, vol. 12(8), pages 1-20, April.
  5. Zhang, H.S. & Shen, X.Y. & Huang, J.P., 2016. "Pattern of trends in stock markets as revealed by the renormalization method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 340-346.
  6. Wendi Zhang & Bin Li & Alan Wee-Chung Liew & Eduardo Roca & Tarlok Singh, 2023. "Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-33, December.
  7. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
  8. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
  9. Lingling Zhou & Jing Xia & Lijing Yu & Ying Wang & Yun Shi & Shunxiang Cai & Shaofa Nie, 2016. "Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans," IJERPH, MDPI, vol. 13(4), pages 1-13, March.
  10. Shao, Zhen & Yang, ShanLin & Gao, Fei & Zhou, KaiLe & Lin, Peng, 2017. "A new electricity price prediction strategy using mutual information-based SVM-RFE classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 330-341.
  11. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
  12. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
  13. Saeed Hajibabaei & Nematollah Hajibabaei & Seyed Mohammad Hoseini & Somaye Hajibabaei & Sajad Hajibabaei, 2014. "Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 18(2), pages 105-130, Spring.
  14. Racine Ly & Fousseini Traore & Khadim Dia, 2021. "Forecasting Commodity Prices Using Long Short-Term Memory Neural Networks," Papers 2101.03087, arXiv.org, revised Jan 2021.
  15. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020. "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper 103813, University Library of Munich, Germany.
  16. Chlebus Marcin & Dyczko Michał & Woźniak Michał, 2021. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem," Central European Economic Journal, Sciendo, vol. 8(55), pages 44-62, January.
  17. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
  18. Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020. "Using Machine Learning to Forecast Future Earnings," Papers 2005.13995, arXiv.org.
  19. Wang, Chao & Lim, Ming K & Zhao, Longfeng & Tseng, Ming-Lang & Chien, Chen-Fu & Lev, Benjamin, 2020. "The evolution of Omega-The International Journal of Management Science over the past 40 years: A bibliometric overview," Omega, Elsevier, vol. 93(C).
  20. Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
  21. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.
  22. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.
  23. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
  24. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
  25. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
  26. Katsafados, Apostolos & Anastasiou, Dimitris, 2022. "Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?," MPRA Paper 111418, University Library of Munich, Germany.
  27. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
  28. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
  29. Jianrong Wei & Jiping Huang, 2012. "An Exotic Long-Term Pattern in Stock Price Dynamics," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-5, December.
  30. Alberto De Santis & Umberto Dellepiane & Stefano Lucidi & Stefania Renzi, 2014. "Optimal Step-wise Parameter Optimization of a FOREX Trading Strategy," DIAG Technical Reports 2014-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
  31. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
  32. Li Xiangfei & Zhang Zaisheng & Huang Chao, 2014. "An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 2(6), pages 481-504, December.
  33. Pal, Shanoli Samui & Kar, Samarjit, 2019. "Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 18-30.
  34. Jing Zhao & Yaoqi Duan & Xiaojuan Liu, 2018. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
  35. Cheng, Gang & Yang, Yuhong, 2015. "Forecast combination with outlier protection," International Journal of Forecasting, Elsevier, vol. 31(2), pages 223-237.
  36. Tamerlan Mashadihasanli, 2022. "Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 439-454, July.
  37. Tripathi Manas & Kumar Saurabh & Inani Sarveshwar Kumar, 2021. "Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications," Journal of Time Series Econometrics, De Gruyter, vol. 13(1), pages 43-71, January.
  38. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
  39. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
  40. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
  41. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
  42. Xiong, Tao & Li, Chongguang & Bao, Yukun, 2017. "Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model," Economic Modelling, Elsevier, vol. 60(C), pages 11-23.
  43. Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.
  44. Maria Glenski & Tim Weninger & Svitlana Volkova, 2019. "Improved Forecasting of Cryptocurrency Price using Social Signals," Papers 1907.00558, arXiv.org.
  45. Jeong, Kwang-Seuk & Kim, Dong-Kyun & Jung, Jong-Mun & Kim, Myoung-Chul & Joo, Gea-Jae, 2008. "Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics," Ecological Modelling, Elsevier, vol. 211(3), pages 292-300.
  46. Mehdi Khashei & Zahra Hajirahimi, 2017. "Performance evaluation of series and parallel strategies for financial time series forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-24, December.
  47. Seung Hwan Jeong & Hee Soo Lee & Hyun Nam & Kyong Joo Oh, 2021. "Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
  48. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
  49. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
  50. S. AL Wadi & Mohammad Almasarweh & Ahmed Atallah Alsaraireh, 2018. "Predicting Closed Price Time Series Data Using ARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 181-181, November.
  51. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
  52. Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
  53. Manahov, Viktor & Hudson, Robert & Linsley, Philip, 2014. "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 299-316.
  54. Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
  55. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
  56. Guo, Zhenhai & Zhao, Jing & Zhang, Wenyu & Wang, Jianzhou, 2011. "A corrected hybrid approach for wind speed prediction in Hexi Corridor of China," Energy, Elsevier, vol. 36(3), pages 1668-1679.
  57. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
  58. Sujin Pyo & Jaewook Lee & Mincheol Cha & Huisu Jang, 2017. "Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
  59. Jing Yu & Feng Ding & Chenghao Guo & Yabin Wang, 2019. "System load trend prediction method based on IF-EMD-LSTM," International Journal of Distributed Sensor Networks, , vol. 15(8), pages 15501477198, August.
  60. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
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  62. Shing-Chih Yang & Chan-Shal Lee & Hsuan-Shih Lee, 2012. "Construction of the prediction model of business operation performance in the electronic industry," E3 Journal of Business Management and Economics., E3 Journals, vol. 3(2), pages 079-089.
  63. XingYu Fu & JinHong Du & YiFeng Guo & MingWen Liu & Tao Dong & XiuWen Duan, 2018. "A Machine Learning Framework for Stock Selection," Papers 1806.01743, arXiv.org, revised Aug 2018.
  64. Bradley J. Pillay & Absalom E. Ezugwu, 2019. "Metaheuristics optimized feedforward neural networks for efficient stock price prediction," Papers 1906.10121, arXiv.org, revised Jun 2020.
  65. Zhen Zeng & Tucker Balch & Manuela Veloso, 2021. "Deep Video Prediction for Time Series Forecasting," Papers 2102.12061, arXiv.org, revised Nov 2021.
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