A hybrid model for financial time‐series forecasting based on mixed methodologies

Zhidan Luo · Wei Guo · Qingfu Liu · Zhengjun Zhang

36 citations0/0 files storedExpert Systems

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

Abstract This paper proposes a hybrid model that combines ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and Taylor expansion using a tracking differentiator to forecast financial time series. Specifically, the financial time series is decomposed by EEMD into some subseries. Then, the linear portion of each subseries is forecasted by the linear ARIMA model, while the nonlinear portion is predicted by the nonlinear Taylor expansion model. The forecasting results of the linear and nonlinear models are combined into the predicted result of each subseries. The final prediction result is obtained by combining the prediction values of all the subseries. The empirical results with real financial time series data demonstrate that this new hybrid approach outperforms the benchmark hybrid models considered in this paper.

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 — 36 citations exist upstream.

References

1986Generalized autoregressive conditional heteroskedasticity 2003Time series forecasting using a hybrid ARIMA and neural network model 2003Financial time series forecasting using support vector machines 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2007A new approach for crude oil price analysis based on Empirical Mode Decomposition 2011Stock index forecasting based on a hybrid model 2011Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction 2017Multidimensional k -nearest neighbor model based on EEMD for financial time series forecasting

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.