Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model

Jin, Yongchao; Wang, Renfang; Zhuang, Xiaodie; Wang, Kenan; Wang, Honglian; Wang, Chenxi; Wang, Xiyin

doi:10.3390/math10214001

Cited by 19 publications

(10 citation statements)

References 11 publications

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“…The first method is to combine the time series model with the machine learning model. For example, Jin et al [24] used the hybrid forecasting model of ARIMA-LSTM to predict the changing trend of COVID-19 in China in the upcoming 50-60 days (11 October 2022 to 9 December 2022). Su [25] used a combined ARIMA-SVR model to perform forecasting analysis of financial markets.…”

Section: Predictive Model Based On a Combination Of Different Methodsmentioning

confidence: 99%

Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model

Shen

et al. 2023

Sustainability

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In this paper, a support vector regression (SVR) adaptive optimization rolling composite model with a sooty tern optimization algorithm (STOA) has been proposed for temperature prediction. Firstly, aiming at the problem that the algorithm tends to fall into the local optimum, the model introduces an adaptive Gauss–Cauchy mutation operator to effectively increase the population diversity and search space and uses the improved algorithm to optimize the key parameters of the SVR model, so that the SVR model can mine the linear and nonlinear information in the data well. Secondly, the rolling prediction is integrated into the SVR prediction model, and the real-time update and self-regulation principles are used to continuously update the prediction, which greatly improves the prediction accuracy. Finally, the optimized STOA-SVR rolling forecast model is used to predict the final temperature. In this study, the global mean temperature data set from 1880 to 2022 is used for empirical analysis, and a comparative experiment is set up to verify the accuracy of the model. The results show that compared with the seasonal autoregressive integrated moving average (SARIMA), feedforward neural network (FNN) and unoptimized STOA-SVR-LSTM, the prediction performance of the proposed model is better, and the root mean square error is reduced by 6.33–29.62%. The mean relative error is reduced by 2.74–47.27%; the goodness of fit increases by 4.67–19.94%. Finally, the global mean temperature is predicted to increase by about 0.4976 °C in the next 20 years, with an increase rate of 3.43%. The model proposed in this paper not only has a good prediction accuracy, but also can provide an effective reference for the development and formulation of meteorological policies in the future.

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Section: Predictive Model Based On a Combination Of Different Methodsmentioning

confidence: 99%

Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model

Shen

et al. 2023

Sustainability

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“…The output of prevention involves considering sentence analysis and decision sentences. In the below table 3 represent prevention by giving suggestion as per rule based outcomes data [33]. 1) Sentence Analysis: Sentence Analysis represents the physical data of the person as glucose level of person fuzzy numbers like Glucose(low, medium or high).…”

Section: Prevention Experimental Detailsmentioning

confidence: 99%

A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System

Ramesh,

Lakshmanna

2024

IEEE Access

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Diabetes is the "mother of all diseases" as it affects multiple organs of body of an individual in some way. Its timely detection and management are critically important. Otherwise, the long run, it can cause several complications in a diabetic. Heart disease is one of the major complications of diabetes.This work proposed an Optimal Scrutiny Boosted Graph Convolutional LSTM (O-SBGC-LSTM), SBGC-LSTM enhanced by Eurygaster Optimization Algorithm (EOA) to tune hyperparameters for early prevention and detection of diabetes disease. This work proposed an Optimal Scrutiny Boosted Graph Convolutional LSTM (O-SBGC-LSTM), SBGC-LSTM enhanced by Eurygaster Optimization Algorithm (EOA) to tune hyperparameters for early prevention and detection of diabetes disease. This method not only captures discriminative features in spatial configuration and temporal dynamics but also explore the co-occurrence relationship between spatial and temporal domains. This method also presents a temporal hierarchical architecture to increase temporal receptive fields of top SBGC-LSTM layer, which boosts the ability to learn high-level semantic representation and significantly reduces computation cost. The performance of O-SBGC-LSTM was found overall to be satisfactory, reaching >98% accuracy in most studies. In comparison with classic machine learning approaches, proposed hybrid DL was found to achieve better performance in almost all studies that reported such comparison outcomes.

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“…To enhance the dependability of their predictions, Kumar et al [22] proposed the use of a spline function to segment the non-linear epidemic time series into different growth stages and predict it at different stages of spread of the infection with a linear modeling approach, which reduces the difficulty of prediction. Some researchers synergistically amalgamated the strengths of several models to devise hybrid models [23][24][25][26][27]. The results found that combining convolutional neural networks with temporal recurrent neural networks (e.g., CNN-LSTM, CNN-GRU) to capture the local spatial correlation and long-term dependence of historical daily new cases has significantly better predictive performance than a single model.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

Ke,

2024

Mathematics

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Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.

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Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model

Cited by 19 publications

References 11 publications

Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model

Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model

A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System

Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

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