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Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques
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this work references cited by second hop

References

  • 2002Multifractal detrended fluctuation analysis of nonstationary time series3,650 cited
  • 2003Financial time series forecasting using support vector machines1,734 cited
  • 2014Predicting stock market index using fusion of machine learning techniques608 cited
  • 2018A novel cryptocurrency price trend forecasting model based on LightGBM518 cited
  • 2004Weighted fuzzy time series models for TAIEX forecasting463 cited
  • 2017An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information458 cited

Cited by

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  • 2020Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods177 cited
  • 2021House price forecasting with neural networks162 cited
  • 2021A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction139 cited
  • 2021A multi-criteria decision making method based on DNMA and CRITIC with linguistic D numbers for blockchain platform evaluation132 cited
  • 2022Hybridization of hybrid structures for time series forecasting: a review126 cited

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2020
CNN-based multivariate data analysis for bitcoin trend prediction
118 cited
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  • 2021Probability transformation of mass function: A weighted network method based on the ordered visibility graph104 cited
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  • 2023A hybrid approach using Z-number DEA model and Artificial Neural Network for Resilient supplier Selection98 cited
  • 2020Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane96 cited
  • 2024Machine learning in business and finance: a literature review and research opportunities96 cited
  • 2023Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods91 cited
  • 2020Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks88 cited