Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model

Zhang, Biao; Chen, Song; Liu, Ying; Jiang, Xuchu

doi:10.1016/j.heliyon.2022.e11670

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…The Prophet model, developed by the Facebook team in 2017, is a powerful tool for time series data forecasting [22]. It can handle time series data with both linear and non-linear growth, as well as multiple seasonality patterns.…”

Section: Prophet Modelmentioning

confidence: 99%

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Chen,

Zheng,

Shi

et al. 2024

BMC Public Health

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Background Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. Method In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. Results The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. Conclusion The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

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Section: Prophet Modelmentioning

confidence: 99%

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Chen,

Zheng,

Shi

et al. 2024

BMC Public Health

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“…Where 𝒌 𝒊 represents the effect of holidays on forecasting values and 𝑫 𝒊 represents dummy variables [14].…”

Section: 𝒚(𝒕) = 𝒈(𝒕) + 𝒔(𝒕) + 𝒉(𝒕) + 𝜺(𝒕)mentioning

confidence: 99%

Predicting Earthquake Magnitudes in Indonesia: Exploring the Potential of the Prophet Algorithm

Nurindahsari,

Wiyono,

Dairoh

2024

Jurnal Ilmu Komputer dan Informasi

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Research on earthquakes has been extensively conducted by previous studies using various methods and specific discussions. Similarly, research to predict the magnitude of earthquakes that will occur in the future has also been conducted. This study employs the Prophet algorithm to test its capability in predicting a case study's magnitude using data with numerous missing values and outliers. The study is conducted without transformation and with Box-Cox and log-transformations. Transformations are applied to handle outliers. The results indicate that across the three experiments, the difference between the predicted and actual data ranges from 0.1 to 0.5 or even more. Performance metrics reveal that the log-transform is superior to the other two experiments, with a smaller MAE of 0.27 and a MAPE of 5.96%. Nevertheless, the use of the Prophet algorithm in this case study needs further investigation with different treatments to achieve more accurate results.

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“…It operates under the tenet that comparable data points typically have comparable target values or belong to the same class. The algorithm measures the similarity between data points using a distance metric, like the Euclidean distance [9] [92]. The steps involved in finding the optimal K-nearest point as shown in Fig.…”

Section: B) Decision Treementioning

confidence: 99%

Machine and Deep Learning Approaches for Plant Disease Detection: A Comprehensive Review

Yogesh V. Chimate

2023

IJRITCC

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People have been using edible foods since ancient times, and they continue to be an essential component of a healthy diet and traditional food systems today. Food crops as a major source of human energy intake, and the challenges they face due to biotic and abiotic stress factors, such as pollution, insects, bacteria, and unfavourable weather conditions. Detecting plant diseases in the early stage is critical for ensuring a stable supply of healthy food, and traditional methods of disease detection by experts are lengthy and have some limitations. The use of Machine and Deep learning is a key aspect of precision farming for crop growth monitoring. Plenty ML strategies, including random forest and support vector machines (SVMs), Convolutional Neural Networks, Deep learning as well as image processing have been used to precisely detect, classify, and predict plant diseases. By leveraging machine learning algorithms, farmers and agricultural experts can accurately detect and diagnose crop diseases, enabling them to take appropriate measures to control and prevent further spread of the disease. This article provides a comprehensive overview of the different AI approaches for plant disease identification and control, drawing on a range of research articles in the field. The application of machine learning in agriculture holds promise for improving crop health and increasing yields and represents an important area of innovation for sustainable agriculture in the future.

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Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model

Cited by 8 publications

References 17 publications

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Predicting Earthquake Magnitudes in Indonesia: Exploring the Potential of the Prophet Algorithm

Machine and Deep Learning Approaches for Plant Disease Detection: A Comprehensive Review

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