Prediction of Chinese Squid Price Index Based on LSTM Model

In this paper, the Long Short Term memory net-work (LSTM) model is applied to predict the Chinese squid price index, aiming to provide accurate price trend information forecast for investors and policy makers in the squid market. First, the historical price data of squid is collected, and the data preprocessing techniques are applied, including time series formatting, sorting and data normalization. After normalizing the data using the MinMaxScaler method to ensure the consistency of data quality and model input, the data is divided into a training set and a test set, of which eighty percent is used for model training and the remaining twenty percent is used for testing. A sequential model consisting of two layers of LSTM and one fully connected layer is constructed to capture the time and dependency characteristics of price series effectively. This LSTM model is fitted and predicted on the training set and the test set respectively to evaluate the predictive performance of the model. Finally, the prediction results of the model were quantitatively evaluated by mean square error (MSE) and mean absolute error (MAE), and the results were normalized to obtain HMAE and HMSE evaluation indexes. The experimental results show that the LSTM model can effectively predict the dynamic change of the squid price index. According to the calculation of the above evaluation indicators, compared with the RNN model, the accuracy of the prediction result of the LSTM on the squid price index is improved within the range of 50%-80%, while compared with the GRU model, the accuracy is improved within the range of 5%-20%. This result further verifies the validity and accuracy of the LSTM model.