Research Article
Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model
@INPROCEEDINGS{10.4108/eai.18-11-2022.2326870, author={Peng Zhou and Fangyi Li}, title={Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model}, proceedings={Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China}, publisher={EAI}, proceedings_a={ICEMME}, year={2023}, month={2}, keywords={arima model; lstm model; net fund value; time series}, doi={10.4108/eai.18-11-2022.2326870} }- Peng Zhou
Fangyi Li
Year: 2023
Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model
ICEMME
EAI
DOI: 10.4108/eai.18-11-2022.2326870
Abstract
The net value of fund is affected in many ways, and researchers attempt to quantify these influences in order to predict future net value by developing various models. Current prediction models typically can only reflect the linear variation law, and their nonlinear characteristics are either poorly handled or selectively ignored, resulting in less accurate prediction results. Based on this, the ARIMA-LSTM hybrid model is used in this paper to predict funds. After preprocessing historical data, the ARIMA model is used to filter out the linear data characteristics, followed by the LSTM model to extract the nonlinear characteristics by residual, and finally superposition the respective prediction values of the two models was performed to obtain the hybrid model's prediction results. The paper's methodologies are empirically proven to be more accurate and applicable than typical fund forecast methods.
