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- W4367016300 abstract "With the rapid development of deep learning, time series models based on neural networks have emerged at this stage, and the time series forecasting problem is one of the important research areas. Traditional time series models are only suitable for short-term forecasting and cannot achieve dynamic forecasting; while most neural network-based forecasting models have poor accuracy and stability for long-term forecasting. In this paper, we propose a bidirectional long short-term memory network (Bi-LSTM) prediction model based on whale optim-ization algorithm (WOA) and attention mechanism, and uses this model to study the global temperature change trend. Five evaluation indexes such as mean square error (MSE), mean absolute error (MAE), and goodness-of-fit <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$R^{2}$</tex> are selected and compared with the commonly used ARIMA model, and it is concluded that the index data of the model proposed in this paper are better than the traditional model, and the goodness-of-fit <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$R^{2}$</tex> can reach 0.92. From the prediction results, it is known that the global temperature will probably reach about <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$20^{mathrm{o}}mathrm{C}$</tex> by the end of the 21st century, which is similar to the predicted global average temperature of the World Meteorological Organization and international database is close to the predicted temperature, and the prediction is good." @default.
- W4367016300 created "2023-04-27" @default.
- W4367016300 creator A5014490795 @default.
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- W4367016300 date "2023-02-24" @default.
- W4367016300 modified "2023-09-30" @default.
- W4367016300 title "Global Temperature Prediction by BiLSTM Model Based on Whale Optimization Algorithm and Attention Mechanism" @default.
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- W4367016300 doi "https://doi.org/10.1109/nnice58320.2023.10105678" @default.
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