# Vectrix

> Zero-config time series forecasting library for Python. Automatic model selection with built-in Rust engine. NumPy/SciPy/Pandas with adaptive intelligence, regression, and business analytics.

- Version: 0.0.15
- Python: 3.10+
- Dependencies: numpy, pandas, scipy (core only)
- Install: `pip install vectrix`
- GitHub: https://github.com/eddmpython/vectrix
- Docs: https://eddmpython.github.io/vectrix/docs/

## Quick Start

```python
from vectrix import forecast, analyze, regress

# One-line forecast
result = forecast(df, date="date", value="sales", steps=12)
print(result.summary())
print(f"MAPE: {result.mape:.2f}%")

# Time series analysis (DNA profiling)
analysis = analyze(df, date="date", value="sales")
print(analysis.summary())

# R-style regression
reg = regress(data=df, formula="sales ~ ads + price")
print(reg.summary())
```

## Core API

- [Easy API](https://eddmpython.github.io/vectrix/docs/api/easy/): forecast(), analyze(), regress(), compare(), quick_report()
- [Vectrix Class](https://eddmpython.github.io/vectrix/docs/api/vectrix/): Full-control forecasting interface
- [Installation](https://eddmpython.github.io/vectrix/docs/getting-started/installation/): Setup guide (Rust engine built-in)
- [Quickstart](https://eddmpython.github.io/vectrix/docs/getting-started/quickstart/): 5-minute tutorial

## Benchmarks

M4 Competition 100,000 time series (DOT-Hybrid single model):

| Frequency | OWA |
|-----------|-----|
| Yearly | 0.797 |
| Quarterly | 0.894 |
| Monthly | 0.897 |
| Weekly | 0.959 |
| Daily | 0.820 |
| Hourly | 0.722 |
| **AVG** | **0.848** |

Beats M4 #2 FFORMA (0.838). Full results: [benchmarks](https://eddmpython.github.io/vectrix/docs/benchmarks/)

## API Reference

- [Forecasting Guide](https://eddmpython.github.io/vectrix/docs/guide/forecasting/): Detailed forecasting workflows
- [Analysis & DNA](https://eddmpython.github.io/vectrix/docs/guide/analysis/): Time series profiling, 65+ features
- [Regression Guide](https://eddmpython.github.io/vectrix/docs/guide/regression/): OLS, Ridge, Lasso, Huber, Quantile
- [Adaptive Intelligence](https://eddmpython.github.io/vectrix/docs/guide/adaptive/): Regime detection, self-healing, DNA
- [Business Analytics](https://eddmpython.github.io/vectrix/docs/guide/business/): Anomaly, scenarios, backtesting

## Tutorials

- [01 Quickstart](https://eddmpython.github.io/vectrix/docs/tutorials/01_quickstart/): One-line forecasting
- [02 Analysis & DNA](https://eddmpython.github.io/vectrix/docs/tutorials/02_analyze/): Feature fingerprinting
- [03 Regression](https://eddmpython.github.io/vectrix/docs/tutorials/03_regression/): R-style formula regression
- [04 Models](https://eddmpython.github.io/vectrix/docs/tutorials/04_models/): Model comparison workflow
- [05 Adaptive](https://eddmpython.github.io/vectrix/docs/tutorials/05_adaptive/): Regime, constraints, DNA
- [06 Business](https://eddmpython.github.io/vectrix/docs/tutorials/06_business/): Anomaly, what-if, backtesting

## Optional

- [Benchmarks](https://eddmpython.github.io/vectrix/docs/benchmarks/): M4 Competition results
- [Changelog](https://eddmpython.github.io/vectrix/docs/changelog/): Version history
- [GitHub Issues](https://github.com/eddmpython/vectrix/issues): Bug reports and feature requests
