Fine-tuning
Fine-tuning is a powerful process for utilizing TimeGPT more effectively. Foundation models such as TimeGPT are pre-trained on vast amounts of data, capturing wide-ranging features and patterns. These models can then be specialized for specific contexts or domains. With fine-tuning, the model’s parameters are refined to forecast a new task, allowing it to tailor its vast pre-existing knowledge towards the requirements of the new data. Fine-tuning thus serves as a crucial bridge, linking TimeGPT’s broad capabilities to your tasks specificities.
Concretely, the process of fine-tuning consists of performing a certain
number of training iterations on your input data minimizing the
forecasting error. The forecasts will then be produced with the updated
model. To control the number of iterations, use the finetune_steps
argument of the forecast method.
1. Import packages
First, we import the required packages and initialize the Nixtla client
👍 Use an Azure AI endpoint
To use an Azure AI endpoint, remember to set also the
base_urlargument:
nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")
2. Load data
| timestamp | value | |
|---|---|---|
| 0 | 1949-01-01 | 112 |
| 1 | 1949-02-01 | 118 |
| 2 | 1949-03-01 | 132 |
| 3 | 1949-04-01 | 129 |
| 4 | 1949-05-01 | 121 |
3. Fine-tuning
Here, finetune_steps=10 means the model will go through 10 iterations
of training on your time series data.
📘 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-1andtimegpt-1-long-horizon.By default,
timegpt-1is used. Please see this tutorial on how and when to usetimegpt-1-long-horizon.
3.1 Control the level of fine-tuning with finetune_depth
It is also possible to control the depth of fine-tuning with the
finetune_depth parameter.
finetune_depth takes values among [1, 2, 3, 4, 5]. By default, it is
set to 1, which means that a small set of the model’s parameters are
being adjusted, whereas a value of 5 fine-tunes the maximum amount of
parameters. Increasing finetune_depth also increases the time to
generate predictions.
Let’s run a small experiment to see how finetune_depth impacts the
performance.
| unique_id | metric | TimeGPT_depth1 | TimeGPT_depth2 | TimeGPT_depth3 | TimeGPT_depth4 | TimeGPT_depth5 | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | mae | 22.805146 | 17.929682 | 21.320125 | 24.944233 | 28.735563 |
| 1 | 0 | mse | 683.303778 | 462.133945 | 678.182747 | 1003.023709 | 1119.906759 |
As you can see, increasing the depth of fine-tuning can improve the performance of the model, but it can make it worse too due to overfitting.
Thus, keep in mind that fine-tuning can be a bit of trial and error. You
might need to adjust the number of finetune_steps and the level of
finetune_depth based on your specific needs and the complexity of your
data. Usually, a higher finetune_depth works better for large
datasets. In this specific tutorial, since we were forecasting a single
series with a very short dataset, increasing the depth led to
overfitting.
It’s recommended to monitor the model’s performance during fine-tuning
and adjust as needed. Be aware that more finetune_steps and a larger
value of finetune_depth may lead to longer training times and could
potentially lead to overfitting if not managed properly.
Remember, fine-tuning is a powerful feature, but it should be used thoughtfully and carefully.
For a detailed guide on using a specific loss function for fine-tuning, check out the Fine-tuning with a specific loss function tutorial.