Search

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Solar irradiance data include temporal information and geospatial information, so solar irradiance prediction can be regarded as a spatiotemporal sequence prediction problem. However, at present, most of the research is based on time series prediction models, and the research studies on spatial-temporal series prediction models are relatively few. Therefore, it is necessary to integrate spatial-temporal information to construct a spatial-temporal sequence prediction model for research. In this paper, the spatial-temporal prediction model based on graph convolutional network (GCN) and long short-term memory network (LSTM) was established for short-term solar irradiance prediction. In this model, solar radiation observatories were modeled as undirected graphs, where each node corresponds to an observatory, and a GCN was used to capture spatial correlations between sites. For each node, temporal features were extracted by using a LSTM. In order to evaluate the prediction performance of this model, six solar radiation observatories located in the Xinjiang region of China were selected; together with widely used persistence model seasonal autoregressive integrated moving average and data-driven prediction models such as convolutional neural network, recurrent neural network, and LSTM, comparisons were made under different seasons and weather conditions. The experimental results show that the average root mean square error of the GCN-LSTM model at the six sites is 62.058 W / m 2 , which is reduced by 9.8%, 14.3%, 6.9%, and 3.3%, respectively, compared with other models; the average MAE is 25.376 W / m 2 , which is reduced by 27.7%, 26.5%, 20.1%, and 11%, respectively, compared with other models; the average R2 is 0.943, which is improved by 1.4%, 2.2%, 0.8%, and 0.4%, respectively, compared with other models. [ABSTRACT FROM AUTHOR]

Background: Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods: Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results: By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively. Conclusions: Comparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number. [ABSTRACT FROM AUTHOR]

• EMD is applied to reduce the complexity of the prediction subsequence. • The SE of the decomposed sub-sequence is induced to improve the prediction precision of wind speed. • LSTM is applied to predict the high frequency sub-sequences. • The ARIMA is employed to predict the low frequency sub-sequences and one residual. • A hybrid EMD-LSTM-ARIMA model is successfully proposed for wind speed prediction. Wind speed is the key factor of wind power generation. With the increase of the proportion of wind power generation in total power generation, the accurate prediction of wind speeds plays an important role in the stable operations of power grids. However, the strong randomness of wind speeds makes it difficult to accurately predict wind speeds. Thus, a wind speed prediction model combining empirical mode decomposition (EMD) with some novel recurrent neural networks (RNN) and the autoregressive integrated moving average (ARIMA) is proposed to solve the problem. The selected RNNs are long short-term memory network (LSTM) and the gated recurrent unit (GRU) network. In this model, EMD is used to decompose the wind speed sequence to reduce the complexity and non-stationary of the series. The entropy of the samples of the sub-sequences after decomposition is calculated. Consequently, LSTM is applied to predict the high frequency sub-sequences with large entropy while the ARIMA is employed to predict the remaining low frequency sub-sequences and one residual. Finally, the prediction results of each sub series are combined to obtain the final prediction results. To verify the accuracy and stability of the model, four wind speed data sets form Inner Mongolia, China, are used to test the proposed methods. Five models are established in four practical cases and their performances are compared with the performances of the proposed model. The results in this paper show the following: (1) the EMD method can improve the wind speed prediction performance when it is combined with LSTM; (2) after decomposition, LSTM is suitable for predicting high complexity subsequences and the ARIMA is suitable for effectively predicting low complexity subsequences based on the different sample entropies; and (3) the root mean squared errors (RMSEs) of the hybrid model on the four wind speed data sets are 0.4163, 0.2085, 0.1613, and 0.2790, respectively, which are basically lower than those of the five models compared. Therefore, it is feasible to apply the hybrid model to wind speed prediction. [ABSTRACT FROM AUTHOR]