A novel global average temperature prediction model——based on GM-ARIMA combination model

Chen, Xiaoxin; Jiang, Zhansi; Cheng, Hao; Zheng, Hongxin; Cai, Danna; Feng, Yuanpeng

doi:10.1007/s12145-023-01179-1

Earth Sci Inform

2023

DOI: 10.1007/s12145-023-01179-1

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A novel global average temperature prediction model——based on GM-ARIMA combination model

Xiaoxin Chen,

Zhansi Jiang,

Hao Cheng

et al.

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2024

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Cited by 2 publications

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Future forecast of global mean surface temperature using machine learning and conventional time series methods

Durhasan,

Pinar,

Uluocak

et al. 2024

Theor Appl Climatol

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Future forecast of global mean surface temperature using machine learning and conventional time series methods

Durhasan,

Pinar,

Uluocak

et al. 2024

Theor Appl Climatol

View full text Add to dashboard Cite

Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering

Ma,

Lin,

Chen

et al. 2024

Electronics

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With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.

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A novel global average temperature prediction model——based on GM-ARIMA combination model

Cited by 2 publications

References 50 publications

Future forecast of global mean surface temperature using machine learning and conventional time series methods

Future forecast of global mean surface temperature using machine learning and conventional time series methods

Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering

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