Name
Abstract | ||
|---|---|---|
We describe our methodology to support time-series forecasts over spatial datasets using the Prophet library. Our approach underpinned by our transfer learning scheme ensures that model instances capture subtle regional variations and converge faster while using fewer resources. Our benchmarks demonstrate the suitability of our methodology. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/Cluster48925.2021.00076 | 2021 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER 2021) |
Keywords | DocType | ISSN |
Time-series forecasting, Prophet, transfer learning, spatiotemporal data | Conference | 1552-5244 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Menuka Warushavithana | 1 | 0 | 0.68 |
| Saptashwa Mitra | 2 | 0 | 0.68 |
| Mazdak Arabi | 3 | 90 | 10.71 |
| F. Jay Breidt | 4 | 0 | 0.34 |
| Sangmi Lee Pallickara | 5 | 170 | 24.46 |
| Shrideep Pallickara | 6 | 837 | 92.72 |