Fractional differentiation and its use in machine learning
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- Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
- Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
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More about this item
Keywords
fractional differentiation; financial time series; stock exchange; artificial neural networks;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-10-19 (Big Data)
- NEP-CMP-2020-10-19 (Computational Economics)
- NEP-ETS-2020-10-19 (Econometric Time Series)
- NEP-ORE-2020-10-19 (Operations Research)
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