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

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 20; pp. 14673 - 14688
• Main Authors: Jiang, Wentao, zhang, Dabin, Ling, Liwen, Cai, Guotao, Zeng, Lling
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Artificial Intelligence; Artificial neural networks; Computational Intelligence; Control; Data Analytics and Machine Learning; Deep learning; Economic impact; Engineering; Food prices; Machine learning; Mathematical Logic and Foundations; Mechatronics; Methods; Parameter estimation; Prices; Robotics; Society; Standard deviation; Time series; Wavelet transforms; Wheat; China; Abnormal fluctuation detection; Convolutional neural network (CNN); Time series image (TSI ); Standard deviation–Slope(SDS)
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-09121-9

In the analysis of agricultural product price time series, the detection of abnormal fluctuations is the primary task. Accurately judging the abnormal fluctuations of agricultural product prices can help farmers avoid potential economic losses in a timely manner and improve the circulation efficiency of agricultural products. This paper introduces a convolutional neural network (CNN) classification model based on time series images (TSI), and identifies abnormal fluctuations in agricultural product prices using improved standard deviation–slope judgment. First, a standard deviation–slope (SDS) time series abnormal fluctuation criterion is proposed. Second, the Markov Transfer Field (MTF) method is used to convert the sparse one-dimensional time series of agricultural product prices into a two-dimensional dense image. Third, the CNN model is used to automatically extract features from time series images containing abnormal fluctuations, and they are divided into two categories: “normal” and “abnormal”; Finally, the performance of the proposed model was evaluated using China’s corn and wheat price dataset. Comparing with other abnormal fluctuation judgment methods, the accuracy of the proposed algorithm is about 20% higher on average. This confirms the applicability of the standard deviation–slope time series Image-Resnet-34 (SDS-TSI-Resnet34) model in practical scenarios. At the end of the paper, some feasible suggestions for the efficient development of agricultural economy are proposed based on the abnormal fluctuation judgment method.