Learn how to create prediction intervals with TimeGPT
timestamp | value | |
---|---|---|
0 | 1949-01-01 | 112 |
1 | 1949-02-01 | 118 |
2 | 1949-03-01 | 132 |
3 | 1949-04-01 | 129 |
4 | 1949-05-01 | 121 |
level
argument.
Note that accepted values are between 0 and 100.
timestamp | TimeGPT | TimeGPT-hi-80 | TimeGPT-hi-90 | TimeGPT-hi-99 | TimeGPT-lo-80 | TimeGPT-lo-90 | TimeGPT-lo-99 |
---|---|---|---|---|---|---|---|
1961-01-01 | 437.84 | 443.69 | 451.89 | 459.28 | 431.99 | 423.78 | 416.40 |
1961-02-01 | 426.06 | 439.42 | 444.43 | 448.94 | 412.70 | 407.70 | 403.19 |
1961-03-01 | 463.12 | 488.83 | 495.92 | 502.31 | 437.41 | 430.31 | 423.93 |
1961-04-01 | 478.24 | 507.77 | 509.72 | 511.47 | 448.72 | 446.77 | 445.02 |
1961-05-01 | 505.65 | 532.89 | 539.32 | 545.12 | 478.41 | 471.97 | 466.18 |
plot
method. To do so, specify the confidence levels to display using the level
argument.
add_history=True
.
cross_validation
method to generate prediction intervals for each time window.