Fine-tuning with a Specific Loss Function

When you fine-tune, the model trains on your dataset to tailor predictions to your specific dataset. You can specify the loss function to be used during fine-tuning using the finetune_loss argument. Below are the available loss functions:

"default": A proprietary function 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

How to Fine-tune with a Specific Loss Function

Step 1: Import Packages and Initialize Client

First, we import the required packages and initialize the Nixtla client.

import pandas as pd
from nixtla import NixtlaClient
from utilsforecast.losses import mae, mse, rmse, mape, smape

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key='my_api_key_provided_by_nixtla'
)

Step 2: Load Data

Load your data and prepare it for fine-tuning. Here, we will demonstrate using an example dataset of air passenger counts.

df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')
df.insert(loc=0, column='unique_id', value=1)

df.head()

	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

Step 3: Fine-Tune the Model

Let’s fine-tune the model on a dataset using the mean absolute error (MAE). For that, we simply pass the appropriate string representing the loss function to the finetune_loss parameter of the forecast method.

timegpt_fcst_finetune_mae_df = nixtla_client.forecast(
    df=df,
    h=12,
    finetune_steps=10,
    finetune_loss='mae',   # Select desired loss function
    time_col='timestamp',
    target_col='value',
)

After training completes, you can visualize the forecast:

nixtla_client.plot(
    df,
    timegpt_fcst_finetune_mae_df,
    time_col='timestamp',
    target_col='value',
)

Explanation of Loss Functions

Now, depending on your data, you will use a specific error metric to accurately evaluate your forecasting model’s performance. Below is a non-exhaustive guide on which metric to use depending on your use case. Mean absolute error (MAE) $\mathrm{MAE}(\mathbf{y}_{\tau}, \mathbf{\hat{y}}_{\tau}) = \frac{1}{H} \sum^{t+H}_{\tau=t+1} |y_{\tau} - \hat{y}_{\tau}|$

Robust to outliers
Easy to understand
You care equally about all error sizes
Same units as your data

Mean squared error (MSE) $\mathrm{MSE}(\mathbf{y}_{\tau}, \mathbf{\hat{y}}_{\tau}) = \frac{1}{H} \sum^{t+H}_{\tau=t+1} (y_{\tau} - \hat{y}_{\tau})^{2}$

You want to penalize large errors more than small ones
Sensitive to outliers
Used when large errors must be avoided
Not the same units as your data

Root mean squared error (RMSE) $\mathrm{RMSE}(\mathbf{y}_{\tau}, \mathbf{\hat{y}}_{\tau}) = \sqrt{\frac{1}{H} \sum^{t+H}_{\tau=t+1} (y_{\tau} - \hat{y}_{\tau})^{2}}$

Brings the MSE back to original units of data
Penalizes large errors more than small ones

Mean absolute percentage error (MAPE) $\mathrm{MAPE}(\mathbf{y}_{\tau}, \mathbf{\hat{y}}_{\tau}) = \frac{1}{H} \sum^{t+H}_{\tau=t+1} \frac{|y_{\tau}-\hat{y}_{\tau}|}{|y_{\tau}|}$

Easy to understand for non-technical stakeholders
Expressed as a percentage
Heavier penalty on positive errors over negative errors
To be avoided if your data has values close to 0 or equal to 0

Symmetric mean absolute percentage error (sMAPE) $\mathrm{SMAPE}_{2}(\mathbf{y}_{\tau}, \mathbf{\hat{y}}_{\tau}) = \frac{1}{H} \sum^{t+H}_{\tau=t+1} \frac{|y_{\tau}-\hat{y}_{\tau}|}{|y_{\tau}|+|\hat{y}_{\tau}|}$

Fixes bias of MAPE
Equally sensitive to over and under forecasting
To be avoided if your data has values close to 0 or equal to 0

With TimeGPT, you can choose your loss function during fine-tuning as to maximize the model’s performance metric for your particular use case.

Experimentation with Loss Function

Let’s run a small experiment to see how each loss function improves their associated metric when compared to the default setting. Let’s split the dataset into training and testing sets.

train = df[:-36]
test = df[-36:]

Next, let’s compute the forecasts with the various loss functions.

losses = ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']

test = test.copy()

for loss in losses:
    preds_df = nixtla_client.forecast(
    df=train,
    h=36,
    finetune_steps=10,
    finetune_loss=loss,
    time_col='timestamp',
    target_col='value')

    preds = preds_df['TimeGPT'].values

    test.loc[:,f'TimeGPT_{loss}'] = preds

Great! We have predictions from TimeGPT using all the different loss functions. We can evaluate the performance using their associated metric and measure the improvement.

loss_fct_dict = {
    "mae": mae,
    "mse": mse,
    "rmse": rmse,
    "mape": mape,
    "smape": smape
}

pct_improv = []

for loss in losses[1:]:
    evaluation = loss_fct_dict[f'{loss}'](test, models=['TimeGPT_default', f'TimeGPT_{loss}'], id_col='unique_id', target_col='value')
    pct_diff = (evaluation['TimeGPT_default'] - evaluation[f'TimeGPT_{loss}']) / evaluation['TimeGPT_default'] * 100
    pct_improv.append(round(pct_diff, 2))

data = {
    'mae': pct_improv[0].values,
    'mse': pct_improv[1].values,
    'rmse': pct_improv[2].values,
    'mape': pct_improv[3].values,
    'smape': pct_improv[4].values
}

metrics_df = pd.DataFrame(data)
metrics_df.index = ['Metric improvement (%)']

metrics_df

	mae	mse	rmse	mape	smape
Metric improvement (%)	8.54	0.31	0.64	31.02	7.36

From the table above, we can see that using a specific loss function during fine-tuning will improve its associated error metric when compared to the default loss function. In this example, using the MAE as the loss function improves the metric by 8.54% when compared to using the default loss function. That way, depending on your use case and performance metric, you can use the appropriate loss function to maximize the accuracy of the forecasts.

INTRODUCTION

SETUP

DATA REQUIREMENTS

FORECASTING

ANOMALY DETECTION

USE CASES

REFERENCE

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Fine-tuning with a Specific Loss Function