Optimization of LSTM Model Based on Sales Volume Prediction
In order to solve the problems of inaccurate sales prediction accuracy and poor data-set,and achieve more accurate and efficient sales prediction results,a feature engineering based Long Short Term Memory(LSTM)neural network sales prediction model is proposed.The LSTM model is a special structure optimized by a recurrent neural network(RNN)model,suitable for dealing with time series related problems.By extracting,filtering,and deriving new feature sequences through fea-ture engineering and fusing them with the original feature sequences,and combining them with LSTM algorithm for sales prediction,the accuracy of LSTM model prediction has been improved.Taking the actual sales data of Rossmann store as an example,it has been confirmed that the optimized feature se-quence set fused with LSTM model for prediction not only improves prediction efficiency but also en-hances the accuracy of prediction results.
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