Railway Project Application of a Time Series Data Analysis and Prediction Algorithm

In recent years, with the rapid development of the Internet of Things, monitoring devices have been increasingly used in the railway construction process. In order to ensure construction safety, real-time data monitoring, anomaly detection, and trend prediction are required during project implementation. Therefore, it is very important for analyzing and predicting the time series data generated by various sensors. This paper uses a combined autoregressive moving average (ARIMA) and long short-term memory (LSTM) model to analyze and predict monitoring data with linear and non-linear components during the railway construction process. Finally, it can predict the data trend and possible abnormalities in the next period of time and provide some reference for the project work.