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Exploring current trends in agricultural commodities forecasting methods through text mining: Developments in statistical and artificial intelligence methods
16 references·0 citing·55 second hop
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References

  • 2017Guidance on Conducting a Systematic Literature Review4,211 cited
  • 2019Smart literature review: a practical topic modelling approach to exploratory literature review470 cited
  • 2021Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis218 cited
  • 2021A systematic review of stock market prediction using machine learning and statistical techniques208 cited
  • 2020A novel text-based framework for forecasting agricultural futures using massive online news headlines179 cited
  • 2017Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China174 cited
  • 2020Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala165 cited
  • 2019A hybrid VMD–BiGRU model for rubber futures time series forecasting140 cited
  • 2022Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India107 cited
  • 2018Prediction of soybean price in China using QR-RBF neural network model101 cited
  • 2023Agricultural Product Price Forecasting Methods: A Review92 cited
  • 2021Forecasting spot prices of agricultural commodities in India: Application of deep‐learning models87 cited
  • 2021Network analysis of corn cash price comovements86 cited
  • 2020Agricultural product price forecasting methods: research advances and trend

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84 cited
  • 2022Dimensions of digital transformation in the context of modern agriculture82 cited
  • 2018A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction70 cited
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