# Methods for Mid-Term Forecasting of Crop Export and Production
## 2 Related Work
The main advantage of this model in comparison with the compositions of models trained using machine learning is the ability to jointly simulate linear and nonlinear dependencies between the inputs and outputs of the network. Paper [12] compares the NARX model with various models of time series analysis in forecasting the harvest area, production, and yield of soybean in Brazil. The results show that the NARX model provides reliable results and outperforms other methods. Although NARX catches non-linear temporal dependencies and shows good accuracy in time series prediction [13], the quality heavily depends on the hyperparameters of the time-delay component. They should be accurately fine-tuned with grid search or similar techniques, which is often hardly feasible. In the case of insufficient tuning of the hyperparameters or small datasets, the prediction accuracy can be lower than for simpler models. For example, the study [14] showed that in some scenarios radial based function (RBF) SVM regressors outperform the NARX models. Article [15] presented a model for forecasting wheat production based on the recurrent network with the Long-Short Term Memory (LSTM) architecture [16]; such models are widely used for the medium-term analysis of time series. A comparison of the proposed model with several analogs was given. The results confirm that the proposed model achieved better forecasting accuracy than other methods, such as ARIMA and feedforward neural networks. A similar approach was also used in the article [17] for medium-term (up to 4 years) price forecasting in the grain market. LSTM networks can also be combined with convolutional networks (CNN) that makes them able to process raw data such as satellite images or spatial data together with the temporal features. Article [18] presents a CNN-LSTM model to predict soybean yield. The experiment shows that the model outperforms separate CNN and LSTM models. However, in the case of long time series, recurrent networks suffer from the gradient vanishing problem, which reduces their performance in comparison with the NARX models [19]. Transformer networks also seem to be prospective to forecast crop market dynamics and production level since they utilize a multi-head attention mechanism and catch long-term dependencies between various special and temporal features [20; 21]. However, there are no studies of applying them for those problems yet. Paper [22] integrates the analysis of trade flow indicators and the sentiment analysis of news articles. Obviously, raw text features cannot be passed directly to a prediction model. Thus they first build AffectiveSpace sentiment text embeddings [23] and use them together with the structured features to train a multilayer feedforward network. The experimentsshow the proposed model is able to recognize the most meaningful changes in the market trend and to achieve positive returns during the simulation. The review shows the mid-term wheat export, and production forecasting requires fairly complex models, which consider various features from the broad context of past time-steps and easily over-fit. Luckily, the studies such as [6] report that the overfitting can be significantly decreased with multiple regularization techniques, such as Bayesian regularization. The use of texts as the information sources for forecasting are also in the scope of research, but they can hardly be digested by prediction models and require applying information extraction techniques. Therefore, we will implement and test several context-aware models based on the sliding window approach, recurrent networks, and the Transformer architecture. Then we apply and test several regularization techniques to make those models more accurate.