Stock Index Prediction Based on Time Series Decomposition and Hybrid Model
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/2/146 |
| _version_ | 1797480633245630464 |
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| author | Pin Lv Qinjuan Wu Jia Xu Yating Shu |
| author_facet | Pin Lv Qinjuan Wu Jia Xu Yating Shu |
| author_sort | Pin Lv |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:02:51Z |
| format | Article |
| id | doaj.art-b72785fb97d7449e9f49af9c4e413cb8 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T22:02:51Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b72785fb97d7449e9f49af9c4e413cb82023-11-23T19:46:45ZengMDPI AGEntropy1099-43002022-01-0124214610.3390/e24020146Stock Index Prediction Based on Time Series Decomposition and Hybrid ModelPin Lv0Qinjuan Wu1Jia Xu2Yating Shu3School of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaThe 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.https://www.mdpi.com/1099-4300/24/2/146stock index forecastingCEEMDANADFARMALSTMhybrid model |
| spellingShingle | Pin Lv Qinjuan Wu Jia Xu Yating Shu Stock Index Prediction Based on Time Series Decomposition and Hybrid Model Entropy stock index forecasting CEEMDAN ADF ARMA LSTM hybrid model |
| title | Stock Index Prediction Based on Time Series Decomposition and Hybrid Model |
| title_full | Stock Index Prediction Based on Time Series Decomposition and Hybrid Model |
| title_fullStr | Stock Index Prediction Based on Time Series Decomposition and Hybrid Model |
| title_full_unstemmed | Stock Index Prediction Based on Time Series Decomposition and Hybrid Model |
| title_short | Stock Index Prediction Based on Time Series Decomposition and Hybrid Model |
| title_sort | stock index prediction based on time series decomposition and hybrid model |
| topic | stock index forecasting CEEMDAN ADF ARMA LSTM hybrid model |
| url | https://www.mdpi.com/1099-4300/24/2/146 |
| work_keys_str_mv | AT pinlv stockindexpredictionbasedontimeseriesdecompositionandhybridmodel AT qinjuanwu stockindexpredictionbasedontimeseriesdecompositionandhybridmodel AT jiaxu stockindexpredictionbasedontimeseriesdecompositionandhybridmodel AT yatingshu stockindexpredictionbasedontimeseriesdecompositionandhybridmodel |