Three textbook baseline forecasts: mean (forecast = average of history), naive (forecast = last value), drift (linear extrapolation from first to last point). Use as a sanity floor - any sophisticated method (SES, Holt, Holt-Winters) should beat the best of these on a backtest, otherwise the extra complexity isn't earning its keep. Returns point forecasts + 95% prediction intervals per Hyndman §3.1.
| Network | Scheme | Amount | Pay To |
|---|---|---|---|
| Base | exact | $0.001000 USDC | 0xaBF4...a9D0 |
| Polygon | exact | $0.001000 USDC | 0xaBF4...a9D0 |
| Arbitrum One | exact | $0.001000 USDC | 0xaBF4...a9D0 |
| eip155:143 | exact | 0.00 tokens | 0xaBF4...a9D0 |
| eip155:43114 | exact | 0.00 tokens | 0xaBF4...a9D0 |
| eip155:1329 | exact | 0.00 tokens | 0xaBF4...a9D0 |
| Optimism | exact | $0.002000 USDC | 0xaBF4...a9D0 |
| eip155:4663 | exact | 0.00 tokens | 0xaBF4...a9D0 |
| Celo | exact | 0.00 tokens | 0xaBF4...a9D0 |
| Base | upto | $0.001000 USDC | 0xaBF4...a9D0 |
| solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp | exact | 0.00 tokens | J7aN3P...3xwg |
| stellar:pubnet | exact | 0.01 tokens | GDNJXC...WWRL |
| algorand:wGHE2Pwdvd7S12BL5FaOP20EGYesN73ktiC1qzkkit8= | exact | 0.00 tokens | C7IIHG...2XIE |