Short-term forecasting of futures market is valuable and is also a technical challenge. In this paper, a hybrid approach for soybean oil futures price forecasting is proposed based on time-series analysis methods. The method combines wavelet transformation and exponential smoothing so that the characteristics of the time series can be captured at different time scales, and forecasting based on exponential smoothing is applied at each time scale. A comparative case study is then conducted that compares the proposed method with other three methods which are an RNN network with Long Short-Term Memory units, a Support-Vector Regression model, and an Exponential Smoothing model without wavelet decomposition to the time series. It could be concluded that the forecasting error performance of ES and Wavelet-ES was better than LSTM and SVR, and the Wavelet-ES achieved the best results for the direction forecasting. The case study provides valuable reference for application of short-term futures price forecasting.

The efficient and accurate collection of agricultural product market information serves as the basis for the effective regulation of agricultural product markets. To achieve a comprehensive, accurate, and timely collection of agricultural product market information, this study puts forth a technique for collecting holographic information of agricultural product markets. A portable human-machine interaction device with an Advanced RISC (reduced instruction set computer) Machines (ARM)-based processor as the core is developed. Holographic information such as agricultural product market trading time, trading place, product name, price, and trading volume can be collected. Via embedded technology and component technology, innovative agricultural product market positioning, and matching, standardized collection, and data processing, and in combination with intelligent algorithms for analysis and early warning, a mobile application terminal for information collection is developed, namely, a holographic information collector for agricultural product markets (named Nongxincai). By using a layered structure, the hardware integrates microprocessor, storage, power, application and communication interface, and human-machine interaction modules. The device has the advantages of miniaturization, the whole-machine power consumption of less than 0.5 W, and continuous operating time of at least 10 h. A supporting software system for Nongxincai has also been developed, in which Microsoft Windows Mobile 6.5 is employed as the operating system, and the CPU frequency is up to 600 MHz. This configuration fully meets the computing requirements of map processing and large-volume data processing and has high compatibility. Nongxincai has been popularized and applied in 12 provinces/municipalities in China. It has played an important role in the monitoring and early warning of different varieties of agricultural products and target prices of soybean and cotton. Keywords: agricultural product market, holographic information, portable, user-interactive, information collector, monitoring and early warning DOI: 10.25165/j.ijabe.20201303.5845 Citation: Xu S W, Li D H, Zhang Y E, Chen W, Zhuang J Y, Liu J J, et al. Development of portable user-interactive holographic information collector for agricultural product markets. Int J Agric & Biol Eng, 2020; 13(3): 143–153.