Research Article
Enterprise Economic Forecasting Method Based on ARIMA-LSTM Model
@INPROCEEDINGS{10.1007/978-3-030-99188-3_4, author={Xiaofei Dong and Xuesen Zong and Peng Li and Jinlong Wang}, title={Enterprise Economic Forecasting Method Based on ARIMA-LSTM Model}, proceedings={Intelligent Technologies for Interactive Entertainment. 13th EAI International Conference, INTETAIN 2021, Virtual Event, December 3-4, 2021, Proceedings}, proceedings_a={INTETAIN}, year={2022}, month={3}, keywords={Enterprise economic IOT ARIMA LSTM Forecast}, doi={10.1007/978-3-030-99188-3_4} }- Xiaofei Dong
Xuesen Zong
Peng Li
Jinlong Wang
Year: 2022
Enterprise Economic Forecasting Method Based on ARIMA-LSTM Model
INTETAIN
Springer
DOI: 10.1007/978-3-030-99188-3_4
Abstract
Enterprise economic forecast is an important part of the development of enterprises, which can help the government to judge the development of enterprises quickly and effectively so as to make scientific decisions of China. With the development of Internet of Things (IOT) technology, enterprise’s IOT data can bring strong data basis to enterprise’s economic forecast. In order to obtain more accurate results of enterprise economic forecasting, a method of enterprise economic forecasting based on Auto regressive Integrated Moving Average and Long Short Term Memory networks (ARIMA-LSTM) model is proposed, which solves the problem that a single forecasting algorithm can only predict according to a single economic development data. The model uses ARIMA model to predict the linear data of time series such as IOT data, and LSTM to predict the nonlinear relationship. Combined with the historical economic data of enterprises, ARIMA-LSTM model is used to predict the future economic development of enterprises. Comparing the prediction results with ARIMA model and ARIMA-LSTM model without IOT data, it is found that the model has the smallest RMSE, MAE and MAPE. The results show that the model can effectively predict the economic situation of enterprises.
