A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM

Zefan Dong · Yonghui Zhou

26 citations0/0 files storedMathematics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Financial time series data are characterized by non-linearity, non-stationarity, and stochastic complexity, so predicting such data presents a significant challenge. This paper proposes a novel hybrid model for financial forecasting based on CEEMDAN-SE and ARIMA- CNN-LSTM. With the help of the CEEMDAN-SE method, the original data are decomposed into several IMFs and reconstructed via sample entropy into a lower-complexity stationary high-frequency component and a low-frequency component. The high-frequency component is predicted by the ARIMA statistical forecasting model, while the low-frequency component is predicted by a neural network model combining CNN and LSTM. Compared to some classical prediction models, our algorithm exhibits superior performance in terms of three evaluation indexes, namely, RMSE, MAE, and MAPE, effectively enhancing model accuracy while reducing computational overhead.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 26 citations exist upstream.

References

2020A CNN–LSTM model for gold price time-series forecasting 2018Financial time series forecasting model based on CEEMDAN and LSTM 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2019CNNpred: CNN-based stock market prediction using a diverse set of variables 2020Deep Learning for Stock Market Prediction 2018An innovative neural network approach for stock market prediction 2020Gold volatility prediction using a CNN-LSTM approach 2022Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.