Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

Pin Lv · Qinjuan Wu · Jia Wei Xu · Yating Shu

57 citations0/0 files storedEntropy

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

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors' decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.

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 — 57 citations exist upstream.

References

2003Time series forecasting using a hybrid ARIMA and neural network model 2017Deep learning with long short-term memory networks for financial market predictions 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models 2018Financial time series forecasting model based on CEEMDAN and LSTM 2014A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

Cited by

2024Forecasting relative returns for S&P 500 stocks using machine learning 2024A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

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.