nixtla-batch-forecaster
Forecast multiple time series in parallel using TimeGPT. Use when processing 10-100+ contracts efficiently. Trigger with 'batch forecast' or 'parallel forecasting'.
npx skills add majiayu000/claude-skill-registry --skill nixtla-batch-forecaster-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nixtla Batch Forecaster Process multiple time series forecasts in parallel with optimized throughput. ## Overview Leverages TimeGPT API to generate forecasts for many time series concurrently. Features parallel batch processing with rate limiting, automatic fallback for failed batches, and optional portfolio-level aggregation. Produces individual forecasts per series plus combined outputs. ## Prerequisites **Required**: - Python 3.8+ - `nixtla`, `pandas`, `tqdm` packages **Environment Variables**: - `NIXTLA_TIMEGPT_API_KEY`: Your TimeGPT API key **Installation**: ```bash pip install nixtla pandas tqdm ``` ## Instructions ### Step 1: Prepare Input Data Your CSV must have the Nixtla schema columns: | Column | Type | Description | |--------|------|-------------| | `unique_id` | string | Series identifier (contract ID) | | `ds` | datetime | Timestamp | | `y` | numeric | Value to forecast | Analyze your data: ```bash python {baseDir}/scripts/prepare_data.py your_data.csv ``` ### Step 2: Set API Key ```bash export NIXTLA_TIMEGPT_API_KEY=your_api_key_here ``` ### Step 3: Run Batch Forecast Execute the batch forecasting engine: ```bash python {baseDir}/scripts/batch_forecast.py your_data
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare Input Data
- Step 2: Set API Key
- Step 3: Run Batch Forecast
- Step 4: Generate Report
- Output
- Error Handling
- Examples
- Example 1: Forecast 50 Daily Contracts
- Example 2: Hourly Portfolio with Aggregation
- Resources
pip install nixtla pandas tqdm
python {baseDir}/scripts/prepare_data.py your_data.csv
export NIXTLA_TIMEGPT_API_KEY=your_api_key_here
python {baseDir}/scripts/batch_forecast.py your_data.csv --horizon 14 --freq D
python {baseDir}/scripts/generate_report.py forecasts/
python {baseDir}/scripts/batch_forecast.py contracts.csv \
python {baseDir}/scripts/batch_forecast.py portfolio.csv \What does the nixtla-batch-forecaster skill do?
Forecast multiple time series in parallel using TimeGPT. Use when processing 10-100+ contracts efficiently. Trigger with 'batch forecast' or 'parallel forecasting'.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-batch-forecaster-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.