Fine-tuning
We can fine-tune TimeGPT by specifying the finetune_steps
parameter.
import pandas as pd
from nixtla import NixtlaClient
nixtla_client = NixtlaClient(
# defaults to os.environ.get("NIXTLA_API_KEY")
api_key = 'my_api_key_provided_by_nixtla'
)
Use an Azure AI endpoint
To use an Azure AI endpoint, remember to set also the
base_url
argument:
nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")
# Read data
df = pd.read_csv("https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv")
# Forecast with fine-tuning.
# Here, we fine-tune for 5 steps
forecast_df = nixtla_client.forecast(
df=df,
h=12,
finetune_steps=5,
time_col='timestamp',
target_col="value"
)
INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: MS
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
Available models in Azure AI
If you are using an Azure AI endpoint, please be sure to set
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
.
For more information on fine-tuning, read our fine-tuning tutorial.
Updated about 2 months ago