Study on forecasting model of apple price combination based on stacked LSTM and entropy method

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.