"default"
- a proprietary loss function that is robust to outliers"mae"
- mean absolute error"mse"
- mean squared error"rmse"
- root mean squared error"mape"
- mean absolute percentage error"smape"
- symmetric mean absolute percentage error👍 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")
finetune_loss
parameter of the forecast
method.
unique_id | timestamp | value | |
---|---|---|---|
0 | 1 | 1949-01-01 | 112 |
1 | 1 | 1949-02-01 | 118 |
2 | 1 | 1949-03-01 | 132 |
3 | 1 | 1949-04-01 | 129 |
4 | 1 | 1949-05-01 | 121 |
finetune_loss
parameter of the forecast
method.
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to setmodel="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
.
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to setGreat! We have predictions from TimeGPT using all the different loss functions. We can evaluate the performance using their associated metric and measure the improvement.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
.
mae | mse | rmse | mape | smape | |
---|---|---|---|---|---|
Metric improvement (%) | 8.54 | 0.31 | 0.64 | 31.02 | 7.36 |