nixtla-timegpt-finetune-lab

name	nixtla-timegpt-finetune-lab
description	Enables TimeGPT model fine-tuning on custom datasets with Nixtla SDK. Guides dataset preparation, job submission, status monitoring, model comparison, and accuracy benchmarking. Activates when user needs TimeGPT fine-tuning, custom model training, domain-specific optimization, or zero-shot vs fine-tuned comparison.
allowed-tools	Read,Write,Glob,Grep,Edit
version	1.0.0
license	MIT

Nixtla TimeGPT Fine-Tuning Lab

Guide users through production-ready TimeGPT fine-tuning workflows.

Overview

This skill manages TimeGPT fine-tuning:

• Dataset preparation: Validate and format training data
• Job submission: Submit fine-tuning jobs to TimeGPT API
• Status monitoring: Track job progress until completion
• Model comparison: Compare zero-shot vs fine-tuned performance

Prerequisites

Required:

• Python 3.8+
• nixtla package
• NIXTLA_API_KEY environment variable

Installation:

pip install nixtla pandas utilsforecast
export NIXTLA_API_KEY='your-api-key'

Get API Key: https://dashboard.nixtla.io

Instructions

Step 1: Prepare Dataset

Ensure data is in Nixtla schema:

python {baseDir}/scripts/prepare_finetune_data.py \
    --input data/sales.csv \
    --output data/finetune_train.csv

Step 2: Configure Fine-Tuning

python {baseDir}/scripts/configure_finetune.py \
    --train data/finetune_train.csv \
    --model_name "sales-model-v1" \
    --horizon 14 \
    --freq D

Step 3: Submit Job

python {baseDir}/scripts/submit_finetune.py \
    --config forecasting/finetune_config.yml

Step 4: Monitor Progress

python {baseDir}/scripts/monitor_finetune.py \
    --job_id <job_id>

Step 5: Compare Models

python {baseDir}/scripts/compare_finetuned.py \
    --test data/test.csv \
    --finetune_id <model_id>

Output

• forecasting/finetune_config.yml: Fine-tuning configuration
• forecasting/artifacts/finetune_model_id.txt: Saved model ID
• forecasting/results/comparison_metrics.csv: Performance comparison

Error Handling

1. Error: NIXTLA_API_KEY not set Solution: Export your API key: export NIXTLA_API_KEY='...'

2. Error: Insufficient training data Solution: Need 100+ observations per series

3. Error: Fine-tuning job failed Solution: Check data format, ensure no NaN values

4. Error: Model ID not found Solution: Verify job completed, check artifacts directory

Examples

Example 1: Basic Fine-Tuning

# Prepare data
python {baseDir}/scripts/prepare_finetune_data.py \
    --input sales.csv --output train.csv

# Submit job
python {baseDir}/scripts/submit_finetune.py \
    --train train.csv \
    --model_name "my-sales-model" \
    --horizon 14

Output:

Fine-tuning job submitted: job_abc123
Model ID saved to: artifacts/finetune_model_id.txt

Example 2: Compare Zero-Shot vs Fine-Tuned

python {baseDir}/scripts/compare_finetuned.py \
    --test test.csv \
    --finetune_id my-sales-model

Output:

Model Comparison:
  TimeGPT Zero-Shot: SMAPE=12.3%
  TimeGPT Fine-Tuned: SMAPE=8.7%
  Improvement: 29.3%

Resources

• Scripts: {baseDir}/scripts/
• TimeGPT Docs: https://docs.nixtla.io/
• Fine-Tuning Guide: https://docs.nixtla.io/docs/finetuning

Related Skills:

• nixtla-schema-mapper: Prepare data before fine-tuning
• nixtla-experiment-architect: Create baseline experiments
• nixtla-usage-optimizer: Evaluate cost-effectiveness