To forecast with TimeGPT, call the forecast
method. Pass your
DataFrame and specify your target and time column names. Then plot the
predictions using the plot
method. You can read about data
requierments
here.
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, set the base_url
argument:
nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")
# Read the data
df = pd.read_csv("https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv")
# Forecast
forecast_df = nixtla_client.forecast(
df=df,
h=12,
time_col='timestamp',
target_col="value"
)
# Plot predictions
nixtla_client.plot(
df=df,
forecasts_df=forecast_df,
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:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
📘 Available models in Azure AI
If you use an Azure AI endpoint, set model="azureai"
nixtla_client.detect_anomalies(..., model="azureai")
For the public API, two models are supported: timegpt-1
and
timegpt-1-long-horizon
.
By default, timegpt-1
is used. See this
tutorial
for details on using timegpt-1-long-horizon
.