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STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM
7 references·9 citing·36 second hop
STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM (2020)STL-ATTLSTM: Vegetable Price Forecasting …AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction (2019)AT-LSTM: An Attention-based L…Stock Price Prediction Using Attention-based Multi-Input LSTM (2018)Stock Price Prediction Using …Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China (2017)Seasonal forecasting of agric…A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017)A Dual-Stage Attention-Based …Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation (2016)Seasonal Adjustment Methods a…Stock Price Prediction Using the ARIMA Model (2014)Stock Price Prediction Using …Forecasting principles and practiceForecasting principles and pr…Machine learning predictions of China commodity price indices (2025)Machine learning predictions …Forecasts of wholesale food price indices through Gaussian process regressions (2025)Forecasts of wholesale food p…Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices (2024)Hidden Markov guided Deep Lea…Wholesale Food Price Index Forecasts with the Neural Network (2023)Wholesale Food Price Index Fo…Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level (2023)Scrap steel price forecasting…Agricultural Product Price Forecasting Methods: A Review (2023)Agricultural Product Price Fo…Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM (2022)Forecasting Agricultural Comm…Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors (2022)Agricultural Price Prediction…A novel STL-based hybrid model for forecasting hog price in China (2022)A novel STL-based hybrid mode…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecasting and Control (1977)Comparing Predictive Accuracy (1994)Edible oil wholesale price forecasts via the neural network (2023)China commodity price index (CCPI) forecasting via the neural network (2025)Machine Learning Green Gram Price Predictions (2025)Deep learning for event-driven stock prediction (2015)Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination (2021)FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks (2021)China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach (2022)Long-term dependence in common stock returns (1977)Intraday periodicity and volatility persistence in financial markets (1997)GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics (2001)Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models (2023)LSTM-ARIMA as a hybrid approach in algorithmic investment strategies (2025)Neural network forecasting for seasonal and trend time series (2003)Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model (2024)Deep Learning Approaches for Potato Price Forecasting: Comparative Analysis of LSTM, Bi-LSTM, and AM-LSTM Models (2024)A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment (2025)The Fundamental Theorem of Exponential Smoothing (1961)The Combination of Forecasts (1969)Asymmetry in Retail, Wholesale, and Shipping Point Pricing for Fresh Vegetables (1982)Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India (2024)A test for independence based on the correlation dimension (1996)Chapter 4 Forecast Combinations (2006)Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series (2019)A systematic review of fundamental and technical analysis of stock market predictions (2019)Stock Market Analysis: A Review and Taxonomy of Prediction Techniques (2019)A CNN-LSTM-Based Model to Forecast Stock Prices (2020)Advancements in predictive analytics: A philosophical and practical overview (2024)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)
this work references cited by second hop

References

  • n.d.Forecasting principles and practice4,364 cited
  • 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction1,646 cited
  • 2014Stock Price Prediction Using the ARIMA Model1,153 cited
  • 2019AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction184 cited
  • 2017Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China174 cited
  • 2016Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation119 cited
  • 2018Stock Price Prediction Using Attention-based Multi-Input LSTM116 cited

Cited by

  • 2025Forecasts of wholesale food price indices through Gaussian process regressions97 cited
  • 2023Agricultural Product Price Forecasting Methods: A Review92 cited
  • 2022Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM87 cited
  • 2023Wholesale Food Price Index Forecasts with the Neural Network71 cited
  • 2023Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level68 cited
  • 2024Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices57 cited
  • 2022Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors

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54 cited
  • 2025Machine learning predictions of China commodity price indices41 cited
  • 2022A novel STL-based hybrid model for forecasting hog price in China37 cited