60 day-ahead forecasting on PM10 in Mumbai (JAN-FEB)
30.14MAETrans.
Evaluation results
| Method | Links | ||||||
|---|---|---|---|---|---|---|---|
| Trans.Model Type=Temporal Model2026.05 | 30.14 | 35.41 | 1.73 | 28.52 | 11.42 | 20.5 | |
| GpGpModel Type=Spatio-Temporal Model2026.05 | 32.79 | 39.44 | 1.64 | 28.03 | 16.4 | 26.74 | |
| ARIMAModel Type=Temporal Model2026.05 | 35.67 | 42.3 | 1.69 | 29.92 | 18.98 | 27.1 | |
| GCSVRModel Type=Proposed2026.05 | 38.4 | 45.83 | 1.88 | 33.03 | 20.44 | 30.68 | |
| STGCNModel Type=Spatio-Temporal Model2026.05 | 41.46 | 48.88 | 1.07 | 34.65 | 22.48 | 31.24 | |
| NBeatsModel Type=Temporal Model2026.05 | 44.87 | 61.07 | 0.97 | 42.34 | 32.81 | 33.11 | |
| GSTARModel Type=Spatio-Temporal Model2026.05 | 49.7 | 58.73 | 2.53 | 49 | 24.85 | 38.53 | |
| STARMAModel Type=Spatio-Temporal Model2026.05 | 54.02 | 63.87 | 2.73 | 55.59 | 27.01 | 39.76 | |
| DeepARModel Type=Temporal Model2026.05 | 58.25 | 66.41 | 2.95 | 60.33 | 10.57 | 49.77 | |
| LSTMModel Type=Temporal Model2026.05 | 60.79 | 70.05 | 3.08 | 65.58 | 30.39 | 48.43 |