How does this skill handle unknown date formats?

The skill includes error handling for date parsing and allows you to manually specify date formats via the --date_format flag if automatic inference fails.

Is the generated code production-ready?

Yes, the skill generates structured Python modules including error handling and validation that can be integrated into production ETL pipelines.

Can I use this with SQL databases?

Yes, it supports SQL sources via SQLAlchemy, allowing you to map query results or entire tables directly to the Nixtla schema.

What is the standard Nixtla schema?

Nixtla requires a long-format dataset containing three specific columns: 'unique_id' (the identifier for the series), 'ds' (datestamps), and 'y' (the numeric target value).

Does it support exogenous variables?

While it focuses on mapping the primary unique_id, ds, and y columns, the inference engine also detects and documents additional exogenous columns for use in forecasting models.

Nixtla Schema Mapper

Name: Nixtla Schema Mapper
Author: intent-solutions-io

byintent-solutions-io

데이터 과학 및 ML

Transforms diverse data sources into Nixtla-compatible formats by automating column inference and Python code generation.

소개

Nixtla Schema Mapper is a specialized tool for data scientists and engineers looking to streamline time-series forecasting workflows. It intelligently analyzes CSV, SQL, and Parquet data sources to identify the required unique_id, ds, and y columns, then generates production-ready Python transformation scripts. Beyond simple mapping, it creates formal schema contracts and performs data quality checks to ensure your datasets are perfectly formatted for Nixtla models like TimeGPT, significantly reducing the manual labor involved in data preparation and ETL pipeline development.

주요 기능

Automated Python code generation for multi-format data pipelines
0 GitHub stars
Intelligent column inference for timestamps, targets, and series IDs
Comprehensive schema contract documentation and validation rules
Support for CSV, Parquet, SQL, and dbt data sources
Built-in data quality checks to prevent forecasting errors

사용 사례

Creating reusable ETL scripts for time-series model benchmarking
Automating schema mapping for FastAPI-based forecasting services
Standardizing raw transactional data for TimeGPT forecasting

소개

주요 기능

Automated Python code generation for multi-format data pipelines
0 GitHub stars
Intelligent column inference for timestamps, targets, and series IDs
Comprehensive schema contract documentation and validation rules
Support for CSV, Parquet, SQL, and dbt data sources
Built-in data quality checks to prevent forecasting errors

사용 사례

Creating reusable ETL scripts for time-series model benchmarking
Automating schema mapping for FastAPI-based forecasting services
Standardizing raw transactional data for TimeGPT forecasting