CGF: A Category Guidance Based PM2.5 Sequence Forecasting Training Framework

作者：Yu, Haomin; Hu, Jilin; Zhou, Xinyuan; Guo, Chenjuan; Yang, Bin; Li, Qingyong^*

来源：IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 10125-10139.

DOI：10.1109/TKDE.2023.3253703

• Forecasting
• Predictive models
• Training
• Fluctuations
• Decoding
• Market research
• Computational modeling
• Category guidance
• PM2.5 concentration forecasting
• spatio-temporal
• temporal

concentration forecasting is important yet challenging. First, complicated local fluctuations in PM2.5 concentrations disturb modeling global trends. Second, forecasting errors are often accumulated through an autoregressive process. To contend with the two challenges, we propose a Category Guidance based PM2.5 sequence Forecasting training framework (CGF) to enhance the performance of existing PM2.5 concentration fore casting models. CGF contains a Category based Representation Learning (CRL) module and a Category based Self-paced Learning (CSL) module, both of which utilize PM2.5 category information that is easily obtained and publicly available. First, CRL employs category information to guide forecasting models to produce more robust hidden representations that are insensitive to local fluctuations, thus alleviating the negative impact of local fluctuations. Second, CSL adaptively selects real PM2.5 concentration values versus autoregressive PM2.5 forecast values when training fore casting models, helping alleviate error accumulations. The CGF framework is applied to existing PM2.5 forecasting models, and the experimental results on two real-world datasets demonstrate that CGF is able to consistently improve the accuracy of existing forecasting models. Furthermore, to validate the generality of CGF, we conduct extensional experiments in two other time-series prediction tasks, including exchange rate forecasting and electricity forecasting. The experimental results also verify the effectiveness of CGF.

• 单位
  北京交通大学