# Methods for Mid-Term Forecasting of Crop Export and Production
## 2 Related Work  
Multilayer neural network models are widely used for forecasting crop export and prediction. We believe this is because those models can naturally integrate heterogeneous market [4], production, and climate features. [PERSON] et al. [5] applied a multilayer feedforward network model to forecast the wheat crop yield at the district level. They focused mainly on revealing the activation function, which would achieve the best accuracy of prediction. In this study, they evaluated various activation functions and recommended some exponential activation functions to improve the accuracy of the yield forecasting models. Experiments show that proposed activation functions are better than the'sigmoid' activation function for agriculture datasets. Past values of various features such as weather conditions, fuel, and fertilizer prices can significantly affect the yield and trade activity. Therefore, time-series prediction models, which can consider those past observations, seem to be the most prospective. For example, Ref. [6] presents a regression model containing a convolutional neural network and recurrent neural network for crop yield prediction. This model is fitted on environmental data and management practices. The model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. The model can be extended with weather conditions, soil conditions, and management practices, which could explain the variation in crop yields. The proposed model achieves low error and outperforms all other methods that were tested. It was also shown that the model generalizes the yield prediction to untested environments without a significant drop in the prediction accuracy. Many studies are devoted to the research and modeling of the international trade dynamics in crops. In [7], principal component analysis (PCA) was used to analyze the primary financial index data of some trade market companies, and the total score of the evaluation index was obtained in this study. Then, the financial and transaction indicators were simultaneously used as the input variables of the stock price prediction research. The experiment results show that the neural network with the Bayesian regularization algorithm has the highest accuracy and can avoid overfitting in the training process. Finally, they compared the proposed PCA-BP neural network and an investment stock selection strategy based on the traditional stock selection analysis method and showed the proposed model is more effective. Article [8] compares the applicability of different models (autoregressive, regression, Holt-Winters) to predict market demand for dairy products based on historical data. Due to the small volume of training samples, the best results in terms of prediction accuracy were obtained using linear regression models. In forecasting the dynamics of prices in agricultural markets, the most promising results were obtained using multilayer feedforward networks. The study [9] was devoted to assessing and comparing the applicability of ARIMA autoregressive models and multilayer neural networks for medium-term forecasting of the export level of cassava starch. Historical data on exports from Thailand from 2001 to 2003 were used as a pilot dataset. It was revealed that multilayer neural networks more accurately allow predicting the level of exports with higher accuracy. A network with a similar architecture was also used [10] for short-term forecasting of prices in the corn market. Another promising approach to forecasting is the use of recurrent neural networks. In the article [11], researchers proposed a nonlinear autoregressive exogenous model NARX that used a multilayer neural network architecture for predicting trade flows. NARX is a time-delayed feedforward network, that is, a network with no feedback loop, which can reduce its predictive ability.