Polypropylene price prediction based on wavelet denoising and long short term memory networks
In order to help tobacco enterprises control production cost better and make purchase price scientifically,this paper uses wavelet denoising and long short term memory network(LSTM)model to predict the price of polypropylene,the main raw mate-rial upstream of tobacco film.Firstly,using wavelet analysis to denoise polypropylene futures data.Then,an LSTM model was con-structed and compared with ARIMA,MLP,and RNN models.Finally,using LSTM model and selecting multiple sets of feature com-binations for price prediction.The results show that the effect of LSTM model prediction based on the combination of closing price,maximum value and minimum value after wavelet analysis is optimal,and wavelet analysis denoising method in dealing with finan-cial data noise is verified to be valid.
long short-term memory networkprice predictionwavelet analysispolypropylene futures
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