Time Series Prediction of Agricultural Products Price Based on Time Alignment of Recurrent Neural Networks

We propose a time series prediction system of agricultural products price based on recurrent neural networks and their time alignment. The purpose of the system is to provide a way to make the price movements stable by sharing the information of price between producers and consumers of agricultural products. For the purpose, we designed a time alignment mechanism of recurrent neural networks to predict time series of prices, where each predicted results is evaluated not only by the accuracy but also by probability distribution of price movement (statistical characteristic). For the stability of agricultural products, price movements have the same importance as accuracy of predicted prices. This is the reason why we focus on not only the accuracy but also probability distribution. Although we show the results where only past price information is used as input of learning for the first step, the framework is designed to reflect other related information such as meteorological and social information on learning in future.