Adjemian, Michael K. 2012. Quantifying the WASDE announcement effect. American Journal of Agricultural Economics 94: 238–56. [CrossRef] Adjemain, Michael K., and Aaron Smith. 2012. Using USDA forecasts to estimate the price flexibility of demand for agricultural commodities. American Journal of Agricultural Economics 94: 978–95. [CrossRef] Bekkerman, Anton, Gary W. Brester, and Mykel Taylor. 2016. Forecasting a Moving Target: The Roles of Quality and Timing for Determining Northern U.S. Wheat Basis. Journal of Agricultural and Resource Economics 41: 25–41.
- Hoffman, Linwood A., and J. Balagtas. 1999. Providing Timely Farm Price Forecasts: Using Wheat Futures Prices to Forecast U.S. Wheat Prices at the Farm Level. Paper presented at the 10th Federal Forecasters Conference, Washington, DC, USA, June 24; p. 13.
Paper not yet in RePEc: Add citation now
- Hoffman, Linwood A., Scott H. Irwin, and Jose I. Toasa. 2007. Forecasting performance of futures price models for corn, soybeans, and wheat. Paper presented at the Annual Meeting of the American Agricultural Economics Association, Portland, OR, USA, July 29–August 1.
Paper not yet in RePEc: Add citation now
- Hoffman, Linwood A., Xiaoli L. Etienne, Scott H. Irwin, Evelyn V. Colino, and Jose I. Toasa. 2015. Forecast Performance of WASDE Price Projections for U.S. Corn. Agricultural Economics 4622: 157–71. [CrossRef] Isengildina-Massa, Olga, and Stephen MacDonald. 2009. U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change; ERR-80; Washington, DC: U.S. Department of Agriculture, Economic Research Service, September.
Paper not yet in RePEc: Add citation now
- Hoffman, Linwood, and Leslie A. Meyer. 2018. Forecasting the U.S. Season-Average Farm Price of Upland Cotton: Derivation of a Futures Price Forecasting Model; CWS-181-01; Washington, DC: U.S. Department of Agriculture, Economic Research Service, September.
Paper not yet in RePEc: Add citation now
- International Journal of Remote Sensing 29: 2211–25. [CrossRef] Wong, Shee Q., and J. Allen Long. 1995. A neural network approach to stock market holding period returns. American Business Review 13: 61–64.
Paper not yet in RePEc: Add citation now
J. Risk Financial Manag. 2024, 17, 143 14 of 15 Jafar, Syed Hasan, Shakeb Akhtar, Hani El-Chaarani, Parvez Alam Khan, and Ruaa Binsaddig. 2023. Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model. Journal of Risk and Financial Management 16: 423. [CrossRef] Kaastra, Iebeling, and Milton Boyd. 1996. Designing a neural network for forecasting financial and economic time series. Neurocomputing 10: 215–36. [CrossRef] Kryzanowski, Lawrence, Michael Galler, and David W. Wright. 1993. Using artificial Neural nets: An approach to the forecasting neural networks to pick stocks. Financial Analysts Journa 49: 21–27.
Kuan, Chung-Ming, and Tung Liu. 1995. Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics 10: 347–64. [CrossRef] Laxmi, Ratna Raj, and Amrender Kumar. 2011. Weather based forecasting model crops yields using neural network approach. Statistics and Applications 9: 55–69.
- Liu, Hui, and Zhihao Long. 2020. An improved deep learning model for predicting stock market price time series. Digital Signal Processing 102: 102741. [CrossRef] Liu, Keyan, Jianan Zhou, and Dayong Dong. 2021. Improving stock price prediction using the long short-term memory model combined with online social networks. Journal of Behavioral and Experimental Finance 30: 100507. [CrossRef] Lu, Wenjie, Jiazheng Li, Jingyang Wang, and Lele Qin. 2021. A CNN-BiLSTM-AM method for stock price prediction. Neural Comput & Applic 33: 4741–53. [CrossRef] Ly, Racine, Fousseini Traore, and Khadim Dia. 2021. Forecasting Commodity Prices Using Long-Short-Term Memory Neural Networks. IFPRI Discussion Paper 2000. Washington, DC: International Food Policy Research Institute. [CrossRef] Meyer, Leslie A. 1998. Factors Affecting the U.S. Farm Price of Upland Cotton. In Cotton and Wool Situation and Outlook; CWS-1998; Washington, DC: U.S. Department of Agriculture, Economic Research Service.
Paper not yet in RePEc: Add citation now
- Mkhabela, Manasah S., and Nkosazana N. Mashinini. 2005. Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA’s-AVHRR. Agricultural and Forest Meteorology 129: 1–9. [CrossRef] Mkhabela, M. S., P. Bullock, S. Raj, S. Wang, and Y. Yang. 2011. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151: 385–93. [CrossRef] Nair, Vinod, and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. Paper presented at the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel, June 22–24; pp. 807–14.
Paper not yet in RePEc: Add citation now
- Oukhouya, Hassan, and Khalid El Himdi. 2023. Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP—For Forecasting the Moroccan Stock Market. Computer Sciences and Mathematics Forum 1: 39.
Paper not yet in RePEc: Add citation now
- Parida, Nirjharinee, Debahuti Mishra, Kaberi Das, Narendra Kumar Rout, and Ganapati Panda. 2021. On deep ensemble CNN–SAE based novel agro-market price forecasting. Evolutionary Intelligence 14: 851–62. [CrossRef] Qiu, Jiayu, Bin Wang, and Changjun Zhou. 2020. Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE 15: e0227222. [CrossRef] Seabe, Phumudzo Lloyd, Claude Rodrigue Bambe Moutsinga, and Edson Pindza. 2023. Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach. Fractal and Fractional 7: 203. [CrossRef] Wall, Lenny, Denis Larocque, and Pierre-Majorique Léger. 2008. The early explanatory power of NDVI in crop yield modelling.
Paper not yet in RePEc: Add citation now
- Wong, F. S., P. Z. Wang, T. H. Goh, and B. K. Quek. 1992. Fuzzy neural systems for stock selection. Financial Analysis Journal 48: 47–52.
Paper not yet in RePEc: Add citation now