Time series to imaging-based deep learning model for detecting abnormal fluctuation in agriculture product price.

Abstract In the analysis of agricultural product price time series, the detection of abnormal fluctuation is the primary task. Accurately judging the abnormal fluctuation of agricultural product prices will give the policy support to the government and also assist farmers increase production and income. A deep convolutional neural network model based on time series image(TSI) is introduced to identify the abnormal fluctuation of agricultural prices under the improved standard deviation-Slope judgment. Markov Transfer Field(MTF) method is used to transform the pre-processed sparse one-dimensional time series of agricultural prices into two-dimensional dense images, and a deep convolutional neural network(CNN) model is used for automatic feature extraction and classification of time series images containing abnormal fluctuations. The empirical evaluation of China's corn and wheat price datasets are performed in our paper, and compared with other abnormal fluctuation judgment methods, the accuracy of the proposed algorithm is about 20% higher on average, which confirms the applicability of the standard deviation-slope time series Image-Resnet-34 (SDS-TSI-Resnet34) model in practical scenarios. Finally, some feasible suggestions for the efficient development of agricultural economy are proposed based on the abnormal fluctuation judgment method proposed in this paper.