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The big data about music history contains information about time and users'behavior. Researchers could predict the trend of popular songs accurately byanalyzing this data. The traditional trend prediction models can better predictthe short trend than the long trend. In this paper, we proposed the improvedLSTM Rolling Prediction Algorithm (LSTM-RPA), which combines LSTM historicalinput with current prediction results as model input for next time prediction.Meanwhile, this algorithm converts the long trend prediction task into multipleshort trend prediction tasks. The evaluation results show that the LSTM-RPAmodel increased F score by 13.03%, 16.74%, 11.91%, 18.52%, compared with LSTM,BiLSTM, GRU and RNN. And our method outperforms tradi-tional sequence models,which are ARIMA and SMA, by 10.67% and 3.43% improvement in F score.Code:https://github.com/maliaosaide/lstm-rpa

LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction