Modal decomposition-based hybrid model for stock index prediction

Lv, Pin; Shu, Yating; Xu, Jia; Wu, Qinjuan

doi:10.1016/j.eswa.2022.117252

Cited by 32 publications

(5 citation statements)

References 28 publications

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“…Since long time series typically exhibit long-term trends and short-term periodic fluctuations, decomposing time series can help the model effectively capture its internal complex temporal dynamics (Lv et al 2022). Unlike traditional methods that extract trend terms through fixed-window moving averages, we use a frequency-domain-based method to decompose time series.…”

Section: Frequency Decompositionmentioning

confidence: 99%

Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction

Zhu,

Guo,

Chen

et al. 2024

AAAI

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Long sequence prediction has broad and significant application value in fields such as finance, wind power, and weather. However, the complex long-term dependencies of long sequence data and the potential domain shift problems limit the effectiveness of traditional models in practical scenarios. To this end, we propose an Adaptive Meta-Learning Probabilistic Inference Framework (AMPIF) based on sequence decomposition, which can effectively enhance the long sequence prediction ability of various basic models. Specifically, first, we decouple complex sequences into seasonal and trend components through a frequency domain decomposition module. Then, we design an adaptive meta-learning task construction strategy, which divides the seasonal and trend components into different tasks through a clustering-matching approach. Finally, we design a dual-stream amortized network (ST-DAN) to capture shared information between seasonal-trend tasks and use the support set to generate task-specific parameters for rapid generalization learning on the query set. We conducted extensive experiments on six datasets, including wind power and finance scenarios, and the results show that our method significantly outperforms baseline methods in prediction accuracy, interpretability, and algorithm stability and can effectively enhance the long sequence prediction capabilities of base models. The source code is publicly available at https://github.com/Zhu-JP/AMPIF.

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Section: Frequency Decompositionmentioning

confidence: 99%

Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction

Zhu,

Guo,

Chen

et al. 2024

AAAI

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“…where Y i is the true value of NCSKEW or DUVOL, and Y i is the predicting value. MSE is a good indicator for evaluating machine learning models [29,30]. Thus, we employ MSE to compare our 11 machine learning models.…”

Section: Evaluation Criterionmentioning

confidence: 99%

A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics

Li,

Xue,

Wei

et al. 2024

Systems

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This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally.

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“…Decompose the complex raw data into relatively stable and regular subsequences, which improves model prediction accuracy (Yue et al 2022 ). The decomposition presents promising results in diverse nonlinear time series applications, such as financial (Lv et al 2022 ), wind energy (Liu et al 2021 ), traffic flow (Tian 2021 ), and air pollution (Liu et al 2020 ). Despite the aforementioned effect, Liu et al ( 2014 ) noted that the first intrinsic mode function (IMF1) obtained using decomposition is highly volatile and irregular, which could affect the overall prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

Zhang

Ling

Zeng

et al. 2023

Environ Sci Pollut Res

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Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one of the major goals of the global effort to protect the ecological environment. However, due to the high complexity and volatility of carbon emission time series, it is hard to forecast carbon emissions effectively. This research offers a novel decomposition-ensemble framework for multi-step prediction of short-term carbon emissions. The proposed framework involves three main steps: (i) data decomposition. A secondary decomposition method, which is a combination of empirical wavelet transform (EWT) and variational modal decomposition (VMD), is used to process the original data. (ii) Prediction and selection: ten models are used to forecast the processed data. Then, neighborhood mutual information (NMI) is used to select suitable sub-models from candidate models. (iii) Stacking ensemble: the stacking ensemble learning method is innovatively introduced to integrate the selected sub-models and output the final prediction results. For illustration and verification, the carbon emissions of three representative EU countries are used as our sample data. The empirical results show that the proposed framework is superior to other benchmark models in predictions 1, 15, and 30 steps ahead, with the mean absolute percentage error (MAPE) of the proposed framework being as low as 5.4475% in Italy dataset, 7.3159% in France dataset, and 8.6821% in Germany dataset.

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Modal decomposition-based hybrid model for stock index prediction

Cited by 32 publications

References 28 publications

Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction

Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction

A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

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