Application of LSTM neural network in the research of cyclical industries: taking the automotive industry as an illustrative case

Ziheng Wang; Heng Quan; Yu Gao

doi:10.54097/8zkrxf08

Authors

Ziheng Wang
Heng Quan
Yu Gao

DOI:

https://doi.org/10.54097/8zkrxf08

Keywords:

LSTM, industry research, automotive industry, time series prediction, Adam optimization.

Abstract

Anticipating future developmental trends within an industry stands as one of the paramount responsibilities for industry researchers. Nevertheless, owing to the intricate and subjective nature inherent in industry research, coupled with researchers usually having to rely on their own subjective judgments when making predictions, the forecasting outcomes frequently prove unsatisfactory. In order to address this issue, this study introduces deep learning technology into the industry research field and employs Long & Short-Term Memory (LSTM) networks to forecast a typical cyclical industry - the automotive industry. In the specific experiment, Adam optimization algorithm is employed to enhance model training speed, followed by utilization of the grid search algorithm for optimal parameter selection. Subsequently, the obtained optimal model is utilized for LSTM multivariate sequence prediction in order to forecast the future development trend of the automotive industry over the next six months. The ultimate outcome demonstrates that the LSTM model accurately anticipated the forthcoming U-shaped rebound trend in the automotive industry, albeit with some limitations in predicting precise numerical values and timing. Nevertheless, it aligns closely with the actual developmental trajectory. This investigation represents a constructive application of deep learning technology in industrial research, and its final findings hold significant implications for future industry research endeavors.

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