👍 Use an Azure AI endpoint To use an Azure AI endpoint, remember to set also thebase_url
argument:nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")
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 |
finetune_depth
.
finetune_depth
to measure the
impact on performance.
finetune_depth
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to setAs mentioned above,model="azureai"
:nixtla_client.forecast(..., model="azureai")
For the public API, we support two models:timegpt-1
andtimegpt-1-long-horizon
. By default,timegpt-1
is used. Please see this tutorial on how and when to usetimegpt-1-long-horizon
.
finetune_depth
controls how many parameters from
TimeGPT are fine-tuned on your particular dataset. If the value is set
to 1, only a few parameters are fine-tuned. Setting it to 5 means that
all parameters of the model will be fine-tuned.
Using a large value for finetune_depth
can lead to better performances
for large datasets with complex patterns. However, it can also lead to
overfitting, in which case the accuracy of the forecasts may degrade, as
we will see from the small experiment below.
unique_id | metric | TimeGPT_depth1 | TimeGPT_depth2 | TimeGPT_depth3 | TimeGPT_depth4 | TimeGPT_depth5 | |
---|---|---|---|---|---|---|---|
0 | 0 | mae | 22.675540 | 17.908963 | 21.318518 | 24.745096 | 28.734302 |
1 | 0 | mse | 677.254283 | 461.320852 | 676.202126 | 991.835359 | 1119.722602 |
finetune_depth
of 2 achieves
the best results since it has the lowest MAE and MSE.
Also notice that with a finetune_depth
of 4 and 5, the performance
degrades, which is a clear sign of overfitting.
Thus, keep in mind that fine-tuning can be a bit of trial and error. You
might need to adjust the number of finetune_steps
and the level of
finetune_depth
based on your specific needs and the complexity of your
data. Usually, a higher finetune_depth
works better for large
datasets. In this specific tutorial, since we were forecasting a single
series with a very short dataset, increasing the depth led to
overfitting.
It’s recommended to monitor the model’s performance during fine-tuning
and adjust as needed. Be aware that more finetune_steps
and a larger
value of finetune_depth
may lead to longer training times and could
potentially lead to overfitting if not managed properly.