A study of short-term forecasting of raw material prices using a combined LSTM-ARIMA model

Shuang Liu

doi:10.1117/12.2679048

25 May 2023 A study of short-term forecasting of raw material prices using a combined LSTM-ARIMA model

Shuang Liu

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 1271213 (2023) https://doi.org/10.1117/12.2679048
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

In relation to influence from the domestic and international environments, the cost of raw materials is frequently subject to multiple seasonalities that are more difficult to predict^[1]. To predict the price of raw materials, it is critical to employ a reliable and scientific model. The autoregressive moving average (ARIMA) model, the long short-term memory (LSTM) model, and the combined LSTM-ARIMA model were used to forecast the spot price trend of the steel in the short term by using data on a steel plate spot price as the research object.The actual data were empirically analyzed using Python software, and the forecasting results of the ARIMA model, the forecasting results of the LSTM-ARIMA model, and the combined LSTM-ARIMA model The comparison results indicate that the LRST-ARIMA model has the highest prediction accuracy, followed by the ARIMA model, whereas the LSTM-ARIMA model has the highest prediction stability. The results show that the use of combined LSTM-ARIMA models to forecast changes in raw material prices not only promotes the application of time series models and deep learning techniques in enterprise forecasting but also effectively helps enterprises make decisions in the raw material procurement business.

Citation Download Citation

Shuang Liu "A study of short-term forecasting of raw material prices using a combined LSTM-ARIMA model", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 1271213 (25 May 2023); https://doi.org/10.1117/12.2679048

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