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Carbon emissions have become particularly urgent with the acceleration of industrialization. Currently, AI-based carbon emission modeling provides essential support for solving the problem of predicting, managing, and optimizing carbon emissions. However, there are still challenges in accurately predicting carbon dioxide emissions and establishing an appropriate carbon emission regulatory model to manage carbon dioxide emissions effectively. Firstly, this paper introduces a framework for learning seasonal and trend representations called DeTF (Decoupling representation for Time and Frequency domain of time series). The framework is designed for time series data of CO 2 emissions used for power generation from 1973 to 2023. Secondly, we use various data enhancement techniques to improve the robustness of the learned representations. Finally, the prediction results establish a tripartite evolutionary game relationship between the state, regulatory agencies, and enterprises. We use MSE and MAE as evaluation metrics, and our model overall outperforms current state-of-the-art end-to-end algorithms. It outperforms the best performing end-to-end prediction method by 27.08% (MSE) in the prediction results. Besides, the time series prediction results are innovatively combined with the tripartite game model to analyze the main factors determining the optimal strategy. • Time domain augmentation boosts robustness with strong and weak methods. • The New DeTF framework splits time and frequency in time series analysis. • The overall loss consists of two main components: the time domain and the frequency domain. • Time series prediction merges with a tripartite game and explores strategies and equilibria. [ABSTRACT FROM AUTHOR]

Cellular networks are important for the success of modern communication systems, which support billions of mobile users and devices. Powered by artificial intelligence techniques, cellular networks are becoming increasingly smarter, and cellular traffic prediction is an important basis for realizing various applications that have originated from this trend. In this survey, we review the relevant studies on cellular traffic prediction and classify the prediction problems as the temporal and spatiotemporal prediction problems. The prediction models with artificial intelligence are categorized into statistical, machine learning, and deep learning models and then compared. Various applications based on cellular traffic prediction are summarized along with their current progress. The potential research directions are pointed out for future research. To the best of our knowledge, this paper is the first comprehensive survey on cellular traffic prediction. • Artificial intelligence for cellular traffic prediction is reviewed comprehensively. • Cellular traffic prediction problems, models, and evaluation metrics are classified. • The potential applications and directions for future research are pointed out. [ABSTRACT FROM AUTHOR]