关键词: LSTM, 熵值法, 基尼系数, Markov链, 价格预测, 苹果价格

Abstract: In order to improve the prediction accuracy of apple price, a new method combining longterm and shortterm memory models (LSTM) with the entropy method was proposed. By exploiting the selflearning characteristic of LSTM and the objective fitting feature information characteristic of the entropy method, a combination model combining stacked LSTM and entropy method was constructed in this paper. Through comparing and analyzing the prediction performance of several combination models, the experimental results showed that:  apple price had obvious spatial conduction effect, and spatial conduction effect had a significant impact on price fluctuation.  After considering the price spatial conduction effect, the prediction accuracy of the combined model of stacked multilayer LSTM and entropy method was 18.81% higher than LSTM, showing the great performance in price prediction. Finally, based on the optimized combination model, the apple price was predicted to verify the effectiveness of the combination model.

Key words:  LSTM, entropy method, Gini coefficient, Markov chain, price prediction, apple price