A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization

Guo, Yuqi; Guo, Jianfeng; Sun, Bingzhen; Bai, Juncheng; Chen, You‐Wei

doi:10.1016/j.asoc.2022.109726

Cited by 34 publications

(6 citation statements)

References 38 publications

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“…When decomposed, add a white noise amplitude of 0.02 times the standard deviation of the original signal, set the average number of processing to 50. The original series was decomposed into eight IMF components [ 25 ], and the effect is shown in Fig 5 , where each IMF presents the influence of different influencing factors on precipitation at different scales. Compared with the EMD IMFs, the CEEMD-processed IMFs do not show the mode mixing that often occurs in EMD, and each IMF contains significantly different characteristic time scales.…”

Section: Resultsmentioning

confidence: 99%

A combined monthly precipitation prediction method based on CEEMD and improved LSTM

Jiang

2023

PLoS ONE

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With the continuous decline of water resources due to population growth and rapid economic development, precipitation prediction plays an important role in the rational allocation of global water resources. To address the non-linearity and non-stationarity of monthly precipitation, a combined prediction method based on complementary ensemble empirical mode decomposition (CEEMD) and a modified long short-term memory (LSTM) neural network was proposed. Firstly, the CEEMD method was used to decompose the monthly precipitation series into a set of relatively stationary sub-sequence components, which can better reflect the local characteristics of the sequence and further understand the nonlinear dynamic characteristics of the sequence. Then, improved LSTM neural networks were employed to predict each sub-sequence. The proposed improvement method optimized the hyper-parameters of LSTM neural networks using particle swarm optimization algorithm, which avoided the randomness of artificial parameter selection. Finally, the predicted results of each component were superimposed to obtain the final prediction result. The proposed method was validated by taking the monthly precipitation data from 1961 to 2020 in Changde City, Hunan Province as an example. The results of the case study show that, compared with other traditional prediction methods, the proposed method can better reflect the trend of precipitation changes and has higher prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

A combined monthly precipitation prediction method based on CEEMD and improved LSTM

Jiang

2023

PLoS ONE

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“…Since the decomposition process usually produces several sub-series, which make the following prediction process time-consuming, this study adopts the Kmeans algorithm to merge the sub-series with similar complexity before the forecasting process. Sample entropy is a measure of the probability of generating a new pattern of change in a time series and is commonly used to characterise the complexity of sequences [30], [42]. Thus, in this study, the sample entropy serves as a measure of the complexity of the decomposed IMFs and as an input to the K-means algorithm.…”

Section: E the Proposed Hdfm Modelmentioning

confidence: 99%

Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition

Li,

Chen,

Sun

et al. 2024

IEEE Access

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The stock market plays an increasingly important role in the global economy. Accurate stock price forecasting not only aids government in predicting economic trends, but also helps investors anticipate higher expected returns. Nevertheless, hurdles such as non-linearity, complexity and high volatility make it a daunting task to predict stock prices. To address this issue, this paper proposes a new hybrid model, termed Hierarchical Decomposition based Forecasting Model (HDFM), to decompose and forecast stock prices in a hierarchical fashion. The model utilises complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for the initial decomposition of stock price time series. To enhance the predictive efficiency, sub-series with similar sample entropy from the decomposition are combined with the K-means clustering method. Through thorough analysis, it is found that the first combined sub-series contains more high-frequency signals. Therefore, the first combined sub-series is subjected to a second decomposition with variational mode decomposition (VMD). Afterwards, the gated recurrent unit (GRU) model is used to predict each of the sub-series individually. The final results are obtained by merging the prediction outcomes. The proposed model has been evaluated on three different stock markets. The experimental results showed that the proposed model outperformed other forecasting methods across all the stock indices. Moreover, ablation studies demonstrated the effectiveness of each individual component within the proposed model.

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“…Ensemble prediction can provide a more comprehensive and reliable forecast of carbon trading prices by taking advantage of the strengths of different models and mitigating their weaknesses (Yang W. et al, 2022;Guo et al, 2022). Ensemble prediction is a powerful technique that can improve the accuracy of stock market prediction (Chullamonthon and Tangamchit, 2023).…”

Section: Ensemble Modelmentioning

confidence: 99%

A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

Li,

Long,

2024

Front. Energy Res.

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Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.

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A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization

Cited by 34 publications

References 38 publications

A combined monthly precipitation prediction method based on CEEMD and improved LSTM

A combined monthly precipitation prediction method based on CEEMD and improved LSTM

Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition

A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

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