unique_id | ds | y | |
---|---|---|---|
0 | H1 | 1 | 605.0 |
1 | H1 | 2 | 586.0 |
2 | H1 | 3 | 586.0 |
3 | H1 | 4 | 559.0 |
4 | H1 | 5 | 511.0 |
metric | TimeGPT | |
---|---|---|
0 | rmse | 1504.474342 |
output_model_id
.
finetuned_model_id
argument.
metric | TimeGPT_zero_shot | TimeGPT_first_finetune | |
---|---|---|---|
0 | rmse | 1504.474342 | 1472.024619 |
NixtlaClient.finetune
method but providing our already fine-tuned model as
finetuned_model_id
, which will take that model and fine-tune it a bit
more. We can also change the fine-tuning settings, like using
finetune_depth=3
, for example.
output_model_id
this time, it got assigned an
UUID.
We can now use this model to forecast.
metric | TimeGPT_first_finetune | TimeGPT_second_finetune | |
---|---|---|---|
0 | rmse | 1472.024619 | 1435.365211 |
NixtlaClient.finetuned_models
method.
as_df=True
.
id | created_at | created_by | base_model_id | steps | depth | loss | model | freq | |
---|---|---|---|---|---|---|---|---|---|
0 | 468b13fb-4b26-447a-bd87-87a64b50d913 | 2024-12-30 17:57:31.241455+00:00 | user | my-first-finetuned-model | 10 | 3 | default | timegpt-1-long-horizon | MS |
1 | my-first-finetuned-model | 2024-12-30 17:57:16.978907+00:00 | user | None | 10 | 1 | default | timegpt-1-long-horizon | MS |
base_model_id
of our second model is our first
model, along with other metadata.
id | created_at | created_by | base_model_id | steps | depth | loss | model | freq | |
---|---|---|---|---|---|---|---|---|---|
0 | 468b13fb-4b26-447a-bd87-87a64b50d913 | 2024-12-30 17:57:31.241455+00:00 | user | my-first-finetuned-model | 10 | 3 | default | timegpt-1-long-horizon | MS |