IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies

Mu, Guangyu; Liao, Zehan; Li, Jiaxue; Qin, Nini; Yang, Ziye

doi:10.1371/journal.pone.0292677

Cited by 5 publications

(1 citation statement)

References 56 publications

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“… Lai & Wang (2023) successfully applied IPSO to enhance the accuracy of short-term passenger flow prediction in rail transit using LSTM neural networks, showcasing feasibility and efficacy. Similarly the IPSO-LSTM public opinion prediction model of Mu et al (2023) significantly enhanced the accuracy of public opinion trend prediction. Ji, Liew & Yang (2021) introduced an IPSO-LSTM model for stock price prediction, demonstrating its superiority over relevant baseline models on the Australian stock market index, including support vector regression, pure LSTM, and PSO-LSTM.…”

Section: Literature Reviewmentioning

confidence: 90%

The application of the propensity score matching method in stock prediction among stocks within the same industry

Li,

Xue

2024

PeerJ Computer Science

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Stock price prediction is crucial in stock market research, yet existing models often overlook interdependencies among stocks in the same industry, treating them as independent entities. Recognizing and accounting for these interdependencies is essential for precise predictions. Propensity score matching (PSM), a statistical method for balancing individuals between groups and improving causal inferences, has not been extensively applied in stock interdependence investigations. Our study addresses this gap by introducing PSM to examine interdependence among pharmaceutical industry stocks for stock price prediction. Additionally, our research integrates Improved particle swarm optimization (IPSO) with long short-term memory (LSTM) networks to enhance parameter selection, improving overall predictive accuracy. The dataset includes price data for all pharmaceutical industry stocks in 2022, categorized into chemical pharmaceuticals, biopharmaceuticals, and traditional Chinese medicine. Using Stata, we identify significantly correlated stocks within each sub-industry through average treatment effect on the treated (ATT) values. Incorporating PSM, we match five target stocks per sub-industry with all stocks in their respective categories, merging target stock data with weighted data from non-target stocks for validation in the IPSO-LSTM model. Our findings demonstrate that including non-target stock data from the same sub-industry through PSM significantly improves predictive accuracy, highlighting its positive impact on stock price prediction. This study pioneers PSM’s use in studying stock interdependence, conducts an in-depth exploration of effects within the pharmaceutical industry, and applies the IPSO optimization algorithm to enhance LSTM network performance, providing a fresh perspective on stock price prediction research.

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Section: Literature Reviewmentioning

confidence: 90%

The application of the propensity score matching method in stock prediction among stocks within the same industry

Li,

Xue

2024

PeerJ Computer Science

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Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain

2024

Heliyon

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Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model

Jiang,

Hu,

et al. 2024

International Journal of Low-Carbon Technologies

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This article discusses the significance and obstacles of short-term power prediction in photovoltaic systems and introduces a hybrid model for photovoltaic short-term power prediction technology based on variational mode decomposition (VMD), convolutional neural network (CNN), improved particle swarm optimization (IPSO) and least squares support vector machine (LSSVM). In the initial stage, the photovoltaic generation signal is decomposed into multiple Intrinsic mode functions (IMFs) using VMD to enhance the extraction of signal time–frequency characteristics. Subsequently, CNN is utilized for feature learning and extraction of each IMF, modeling the nonlinear and non-stationary features. Following this, the IPSO-LSSVM optimization algorithm is employed to establish and optimize multiple LSSVM models, predicting power fluctuations at different time scales. Finally, the predictions from each model are synthesized to obtain the final photovoltaic short-term power forecast. Through validation with actual photovoltaic generation data, this hybrid model demonstrates high accuracy and reliability in short-term power prediction, showing an average relative error and root mean square error reduction of 15.23 and 53.60%, respectively, compared to a certain comparative model. This proposed method based on VMD-CNN-IPSO-LSSVM hybrid model for photovoltaic power prediction holds promising prospects and practical value in the operation and scheduling of photovoltaic generation systems.

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IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies

Cited by 5 publications

References 56 publications

The application of the propensity score matching method in stock prediction among stocks within the same industry

The application of the propensity score matching method in stock prediction among stocks within the same industry

Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain

Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model

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