两种角度，利用ARIMA、LSTM、SVR、MLP 这4 种模型，通过吉林省水稻产量、病虫害
析。【结果】基于ARIMA 模型和LSTM 模型的时间序列预测结果较好，横截面数据预测中，
|Comparative analysis of rice yield forecasting based on time seriesanalysis and cross-sectional prediction in Jilin Province of China
Chen Wei1, Qi Weiyan1, Yuan Fuxiang2, Li Zhemin※1
1.Agricultural Information Institute，Chinese Academy of Agricultural Sciences，Beijing 100081，China;2.Jilin Institute of Meteorological Sciences，Changchun 130062，China
|［Purpose］Rice is one of the main food crops in China. Rice yield greatly contributes to
China’s food security. Rice yield forcasting is of great significance to China’s agricultural development.
Traditional approaches use time series or cross-sectional data， and apply statistical methods or machine
learning methods to predict rice yield. However，the comparative analyses between multiple methods are still
lacking. This paper aims to compare the differences between the model’s forecasting performance，and to
analyze the usability of models in specific circumstances，in order to better provide implications for national
food security issues.［ Methods］In this paper，we use multiple data sources，including historical rice
yield，disease and pest outbreak，and other characteristics data，to forecast rice yield in Jilin province，
China. Four models are used to forecast rice yield. ARIMA and LSTM are utilized for time series
forecasting. Cross-sectional prediction is also conducted using SVR and MLP. The forecasting results
of different models are analyzed and compared.［ Results］Both ARIMA and LSTM have resulted in
a good forecasting performance using time series data. The forecasting performance would be further
improved if a principle component analysis is applied to reduce the dimensionality of the original data.
［Conclusion］Comparing to the cross-sectional data forecasting，time series data forecasting has
achieved a better performance. This indicates that a rational selection of data and model would improve
the performance in rice yield forecasting.
|Key words: rice yield；ARIMA；LSTM；SVR；MLP；time series