Author(s)

Siqi Chen ¹

Affiliation(s)

¹ Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou, 311508, China

Corresponding Author

Siqi Chen

ABSTRACT

With the rapid change of global macroeconomic condition and the adjustment of industrial structure, to carry out research on technology of dynamic prediction of power material prices is necessary. This paper uses a long- and short-term memory model to forecast the material price in order to adapt to new industrial structure and as a result to improve material price forecasting ability of power companies. The prediction experiments of material price and its fluctuation range are carried out, including the prediction experiment of electric power material benchmark price and the prediction experiment of electric power material price fluctuation range. Based on the experimental results, the prediction results of LSTM model can meet the requirements of improving the ability of material price prediction and fluctuation range prediction of power companies.

KEYWORDS

Long- and short-term memory model, Prediction technology, Material price

CITE THIS PAPER

Siqi Chen, Research on prediction technology of material price based on long- and short-term memory model. Information Systems and Economics (2025) Vol. 6: 1-7. DOI: http://dx.doi.org/10.23977/infse.2025.060101.

REFERENCES

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[4] Yuan M. & Sun R.(2021). Research on price prediction of agricultural commodities based on LSTM neural network. Straits Technology and Industry (11),43-47+60.
[5] Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation (pp. 106-112). IEEE.
[6] Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014. 
[7] Rather, A. M., Agarwal, A., & Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234-3241.
[8] Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4), 1754-1756.
[9] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9, 1735-1780.

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