MVAR forecasts six pollutants over 75 North China cities for up to 120 hours from two input steps, using autoregressive rollout, step-weighted loss, and meteorological cross-attention, and reports lower RMSE than baseline models.
Probing the capacity of a spatiotem- poral deep learning model for short-term pm2
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
MVAR: MultiVariate AutoRegressive Air Pollutants Forecasting Model
MVAR forecasts six pollutants over 75 North China cities for up to 120 hours from two input steps, using autoregressive rollout, step-weighted loss, and meteorological cross-attention, and reports lower RMSE than baseline models.