@inproceedings{674dd9dd28994c4887d3fe89f375a1e4,
title = "High and Low Prices Prediction of Soybean Futures with LSTM Neural Network",
abstract = "The prediction of futures prices is a great challenge. On the other hand, it can bring investors great profits. Most researches just show the predictions of closing prices but we can also predict high and low prices. The high and low prices have lower noises than closing prices, making it easier to predict them and to use them for making profitable strategies. In this paper, we build a model to predict high and low prices of soybean futures with the LSTM neural network using the dataset from the Dalian Commodity Exchange. Then we use mean absolute error (MAE) and trend accuracy to evaluate the performance of this model. For comparison, we predict the closing price using the LSTM neural network and build another prediction model based on the BP neural network. Results show that we get higher accuracy predicting the trends of high and low prices. Also, the prediction model based on the LSTM neural network performs better and it gets more than 80\% of the accuracy in trend estimation when the predicting high prices or low prices have high volatilities.",
keywords = "LSTM neural network, high prices, low prices, price prediction, soybean futures",
author = "Chenhao Wang and Qiang Gao",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018 ; Conference date: 23-11-2018 Through 25-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICSESS.2018.8663896",
language = "英语",
series = "Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS",
publisher = "IEEE Computer Society",
pages = "140--143",
editor = "Li Wenzheng and Babu, \{M. Surendra Prasad\}",
booktitle = "ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science",
address = "美国",
}