Finetuning with a custom loss function

When fine-tuning, we can specify a loss function to be used usin the finetune_loss argument.

The possible values are:

• "mae"

• "mse"

• "rmse"

• "mape"

• "smape"

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'
)

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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")

# Fine-tune with a specified loss function and make predictions
forecast_df = nixtla_client.forecast(
    df=df,
    h=12,
    finetune_steps=5,
    finetune_loss="mae",
    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...

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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 and timegpt-1-long-horizon.

By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.

For more details on specifying a loss function and how it impacts the performance of the model, read our in-depth tutorial on Fine-tuning with a specific loss function.