Add categorical variables
TimeGPT supports categorical variables and we can create them using SpecialDates
.
import pandas as pd
import datetime
from nixtla import NixtlaClient
from nixtla.date_features import SpecialDates
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 the data
df = pd.read_csv("https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv")
# Create categorical variables to label Christmas and summer vacations
categories_dates = SpecialDates(
special_dates={
'christmas_vacations': [datetime.date(year, 12, 1) for year in range(1949, 1960 + 1)],
'summer_vacations': [datetime.date(year, month, 1) for year in range(1949, 1960 + 1) for month in (6, 7)]
}
)
dates = pd.date_range('1949-01-01', '1960-12-01', freq='MS')
categories_df = categories_dates(dates).reset_index(drop=True)
# Merge with the dataset
cat_df = pd.concat([df, categories_df], axis=1)
# Forecast
forecast_df = nixtla_client.forecast(
df=cat_df,
h=24,
target_col='value',
time_col='timestamp'
)
INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: MS
WARNING:nixtla.nixtla_client:You did not provide X_df. Exogenous variables in df are ignored. To surpress this warning, please add X_df with exogenous variables: christmas_vacations, summer_vacations
WARNING:nixtla.nixtla_client:The specified horizon "h" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
INFO:nixtla.nixtla_client:Restricting input...
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 a detailed guide on using categorical variables for forecasting, read our in-depth tutorial on Categorical variables.
Updated 4 months ago