Optimized LSTM Time Series Prediction Model for Industrial Big Data
Yangzhi Li · 2024
In industrial production, a large number of different data streams that gradually distribute with time bring adaptive challenges to industrial big data time series classification algorithms. Puts forward the multivariable tuning LSTM algorithm. This paper also explains the multivariable tuning LSTM algorithm from multiple angles, including the structure and module design of the algorithm and the tuning process of the algorithm. Compared with continuous data prediction algorithm, the experiment shows that the algorithm proposed in this paper can effectively improve the prediction accuracy. Moreover, the experiment verifies that the proposed algorithm model is more suitable for predicting industrial time series data to some extent, and the prediction ability of industrial time series data forecasting system formed by this prediction algorithm is further improved.