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Optimal forecast combination based on PSO-CS approach for daily agricultural future prices forecasting
16 references·2 citing·48 second hop
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References

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  • 2012A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition745 cited
  • 2019Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques388 cited
  • 2016Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market230 cited
  • 2015A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting204 cited
  • 2019Commodity-price comovement and global economic activity201 cited
  • 2020A novel text-based framework for forecasting agricultural futures using massive online news headlines179 cited
  • 2018Oil price forecasting using a hybrid model176 cited
  • 2017Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China174 cited
  • 2019A hybrid VMD–BiGRU model for rubber futures time series forecasting140 cited
  • 2018Optimal Forecast Combination Based on Neural Networks for Time Series Forecasting135 cited
  • 2021The impact of Covid-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels134 cited
  • 2015A neural network based linear ensemble framework for time series forecasting131 cited

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  • 2015Grey double exponential smoothing model and its application on pig price forecasting in China119 cited
  • 2014Stock Price Prediction based on SSA and SVM108 cited
  • 2018A combined neural network model for commodity price forecasting with SSA79 cited
  • Cited by

    • 2024Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model37 cited
    • 2025Predicting Prices of Staple Crops Using Machine Learning: A Systematic Review of Studies on Wheat, Corn, and Rice28 cited