v0.0.3 릴리즈: Rust Turbo Mode, 샘플 데이터셋, pandas 2.x 호환성

Author: eddmpython

CHANGELOG.md

@@ -5,6 +5,49 @@ All notable changes to Vectrix will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.0.3] - 2026-02-28
+
+Performance release — Rust-accelerated core loops (vectrix-core), built-in sample datasets, and pandas 2.x compatibility fixes.
+
+### Added
+
+**Rust Turbo Mode (vectrix-core)**
+- Native Rust extension for core forecasting hot loops via PyO3 + maturin
+- 9 accelerated functions: `ets_filter`, `ets_loglik`, `css_objective`, `seasonal_css_objective`, `ses_sse`, `ses_filter`, `theta_decompose`, `arima_css`, `batch_ets_filter`
+- 3-tier fallback: Rust > Numba JIT > Pure Python — transparent, no code changes needed
+- Pre-built wheels for Linux (manylinux), macOS (x86 + ARM), Windows (x86_64), Python 3.10-3.13
+- Install via `pip install "vectrix[turbo]"` — no Rust compiler needed for users
+- GitHub Actions CI workflow (`publish-core.yml`) for automated wheel builds on `core-v*` tags
+
+**Built-in Sample Datasets**
+- 7 deterministic sample datasets for quick testing: `airline` (144 monthly), `retail` (730 daily), `stock` (252 business daily), `temperature` (1095 daily), `energy` (720 hourly), `web` (180 daily), `intermittent` (365 daily)
+- `loadSample(name)`: Load a sample dataset as DataFrame
+- `listSamples()`: List all available datasets with metadata
+- 41 tests covering all datasets
+
+### Changed
+
+**Performance Improvements**
+- AutoETS: 348ms → 32ms (10.8x faster with Rust turbo)
+- AutoARIMA: 195ms → 35ms (5.6x faster)
+- Theta: 1.3ms → 0.16ms (8.1x faster)
+- `forecast()` end-to-end: 295ms → 52ms (5.6x faster)
+- ETS filter hot loop: 0.17ms → 0.003ms (67x faster)
+- ARIMA CSS objective: 0.19ms → 0.001ms (157x faster)
+
+### Fixed
+
+- pandas 2.x frequency deprecation: `"M"` → `"ME"`, `"Q"` → `"QE"`, `"Y"` → `"YE"`, `"H"` → `"h"`
+
+### Changed (Docs)
+
+- Complete bilingual docs site (EN/KO) with i18n plugin
+- Installation guide updated with Rust Turbo Mode section
+- README updated with turbo benchmarks, sample datasets, comparison table
+- 387 tests (up from 346), 5 skipped (optional dependency guards)
+
+[0.0.3]: https://github.com/eddmpython/vectrix/compare/v0.0.2...v0.0.3
+
 ## [0.0.2] - 2026-02-28
 
 Feature expansion release — Foundation Model wrappers, deep learning models, multivariate forecasting, probabilistic distributions, multi-country holidays, and pipeline system.

README.md

@@ -15,14 +15,14 @@
 <p>
 <img src="https://img.shields.io/badge/3-Dependencies-818cf8?style=for-the-badge&labelColor=0f172a" alt="Dependencies">
 <img src="https://img.shields.io/badge/Pure-Python-6366f1?style=for-the-badge&labelColor=0f172a" alt="Pure Python">
-<img src="https://img.shields.io/badge/No-Compiled%20Extensions-a78bfa?style=for-the-badge&labelColor=0f172a" alt="No Compiled Extensions">
+<img src="https://img.shields.io/badge/Rust-Turbo%20Mode-e45a33?style=for-the-badge&labelColor=0f172a&logo=rust&logoColor=white" alt="Rust Turbo">
 </p>
 
 <p>
 <a href="https://pypi.org/project/vectrix/"><img src="https://img.shields.io/pypi/v/vectrix?style=for-the-badge&color=6366f1&labelColor=0f172a&logo=pypi&logoColor=white" alt="PyPI"></a>
 <a href="https://pypi.org/project/vectrix/"><img src="https://img.shields.io/pypi/pyversions/vectrix?style=for-the-badge&labelColor=0f172a&logo=python&logoColor=white" alt="Python"></a>
 <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-22d3ee?style=for-the-badge&labelColor=0f172a" alt="License"></a>
-<img src="https://img.shields.io/badge/Tests-275%20passed-10b981?style=for-the-badge&labelColor=0f172a&logo=pytest&logoColor=white" alt="Tests">
+<img src="https://img.shields.io/badge/Tests-387%20passed-10b981?style=for-the-badge&labelColor=0f172a&logo=pytest&logoColor=white" alt="Tests">
 </p>
 
 <br>
@@ -101,9 +101,35 @@ A pure-numpy HMM (Baum-Welch + Viterbi) detects regime shifts. When a regime cha
 
 Bottom-up, top-down, and MinTrace reconciliation for hierarchical time series.
 
+### Rust Turbo Mode
+
+Install `vectrix[turbo]` to unlock Rust-accelerated core loops. No Rust compiler needed — pre-built wheels for Linux, macOS (x86 + ARM), and Windows.
+
+| Component | Without Turbo | With Turbo | Speedup |
+|:----------|:-------------|:-----------|:--------|
+| `forecast()` 200pts | 295ms | **52ms** | **5.6x** |
+| AutoETS fit | 348ms | **32ms** | **10.8x** |
+| AutoARIMA fit | 195ms | **35ms** | **5.6x** |
+| ETS filter (hot loop) | 0.17ms | **0.003ms** | **67x** |
+
+Turbo is fully optional. Without it, Vectrix falls back to Numba JIT (if available) or pure Python. Your code doesn't change — just install and it's faster.
+
+### Built-in Sample Datasets
+
+7 ready-to-use datasets for quick testing:
+
+```python
+from vectrix import loadSample, forecast
+
+df = loadSample("airline")       # 144 monthly observations
+result = forecast(df, date="date", value="passengers", steps=12)
+```
+
+Available: `airline`, `retail`, `stock`, `temperature`, `energy`, `web`, `intermittent`
+
 ### Everything is Pure Python
 
-All of the above — forecasting models, regime detection, regression diagnostics, constraint enforcement, hierarchical reconciliation — is implemented in pure Python with only NumPy, SciPy, and Pandas. No compiled extensions, no system dependencies.
+All of the above — forecasting models, regime detection, regression diagnostics, constraint enforcement, hierarchical reconciliation — is implemented in pure Python with only NumPy, SciPy, and Pandas. No compiled extensions, no system dependencies. Rust turbo is optional and never required.
 
 <br>
 
@@ -114,9 +140,10 @@ pip install vectrix
 ```
 
 ```python
-from vectrix import forecast
+from vectrix import forecast, loadSample
 
-result = forecast("sales.csv", steps=12)
+df = loadSample("airline")
+result = forecast(df, date="date", value="passengers", steps=12)
 print(result)
 result.plot()
 ```
@@ -128,11 +155,13 @@ result.plot()
 | | Vectrix | statsforecast | Prophet | Darts |
 |:--|:--:|:--:|:--:|:--:|
 | **Pure Python (no C/Fortran)** | ✅ | ❌ (numba) | ❌ (cmdstan) | ❌ (torch) |
+| **Optional Rust acceleration** | ✅ (5-10x) | ❌ | ❌ | ❌ |
 | **Dependencies** | 3 | 5+ | 10+ | 20+ |
 | **Auto model selection** | ✅ | ✅ | ❌ | ❌ |
 | **Flat-line defense** | ✅ | ❌ | ❌ | ❌ |
 | **Business constraints** | 8 types | ❌ | ❌ | ❌ |
 | **Built-in regression** | R-style | ❌ | ❌ | ❌ |
+| **Sample datasets** | 7 built-in | ❌ | ❌ | ✅ |
 
 <br>
 
@@ -224,6 +253,7 @@ result.plot()
 
 ```bash
 pip install vectrix                # Core (numpy + scipy + pandas)
+pip install "vectrix[turbo]"       # + Rust acceleration (5-10x speedup)
 pip install "vectrix[numba]"       # + Numba JIT (2-5x speedup)
 pip install "vectrix[ml]"          # + LightGBM, XGBoost, scikit-learn
 pip install "vectrix[all]"         # Everything
@@ -382,7 +412,11 @@ vectrix/
 ├── hierarchy/             Bottom-up, top-down, MinTrace
 ├── intervals/             Conformal + bootstrap intervals
 ├── ml/                    LightGBM, XGBoost wrappers
-└── global_model/          Cross-series forecasting
+├── global_model/          Cross-series forecasting
+└── datasets.py            7 built-in sample datasets
+
+rust/                         Optional Rust acceleration (vectrix-core)
+└── src/lib.rs             ETS, ARIMA, Theta, SES hot loops (PyO3)
 ```
 
 <br>

README_KR.md

@@ -15,14 +15,14 @@
 <p>
 <img src="https://img.shields.io/badge/3-Dependencies-818cf8?style=for-the-badge&labelColor=0f172a" alt="Dependencies">
 <img src="https://img.shields.io/badge/Pure-Python-6366f1?style=for-the-badge&labelColor=0f172a" alt="Pure Python">
-<img src="https://img.shields.io/badge/No-Compiled%20Extensions-a78bfa?style=for-the-badge&labelColor=0f172a" alt="No Compiled Extensions">
+<img src="https://img.shields.io/badge/Rust-Turbo%20Mode-e45a33?style=for-the-badge&labelColor=0f172a&logo=rust&logoColor=white" alt="Rust Turbo">
 </p>
 
 <p>
 <a href="https://pypi.org/project/vectrix/"><img src="https://img.shields.io/pypi/v/vectrix?style=for-the-badge&color=6366f1&labelColor=0f172a&logo=pypi&logoColor=white" alt="PyPI"></a>
 <a href="https://pypi.org/project/vectrix/"><img src="https://img.shields.io/pypi/pyversions/vectrix?style=for-the-badge&labelColor=0f172a&logo=python&logoColor=white" alt="Python"></a>
 <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-22d3ee?style=for-the-badge&labelColor=0f172a" alt="License"></a>
-<img src="https://img.shields.io/badge/Tests-275%20passed-10b981?style=for-the-badge&labelColor=0f172a&logo=pytest&logoColor=white" alt="Tests">
+<img src="https://img.shields.io/badge/Tests-387%20passed-10b981?style=for-the-badge&labelColor=0f172a&logo=pytest&logoColor=white" alt="Tests">
 </p>
 
 <br>
@@ -101,9 +101,35 @@ print(report.summary())
 
 Bottom-up, Top-down, MinTrace로 계층적 시계열을 조정합니다.
 
+### Rust Turbo Mode
+
+`vectrix[turbo]`를 설치하면 Rust로 작성된 핵심 루프가 활성화됩니다. Rust 컴파일러 불필요 — Linux, macOS (x86 + ARM), Windows용 사전 빌드 wheel 제공.
+
+| 구성요소 | Turbo 없음 | Turbo 적용 | 속도 향상 |
+|:---------|:----------|:----------|:---------|
+| `forecast()` 200pts | 295ms | **52ms** | **5.6x** |
+| AutoETS fit | 348ms | **32ms** | **10.8x** |
+| AutoARIMA fit | 195ms | **35ms** | **5.6x** |
+| ETS 필터 (핫 루프) | 0.17ms | **0.003ms** | **67x** |
+
+Turbo는 완전 선택사항입니다. 없으면 Numba JIT(설치 시) 또는 순수 Python으로 동작합니다. 코드 변경 불필요 — 설치만 하면 자동으로 빨라집니다.
+
+### 내장 샘플 데이터셋
+
+즉시 테스트 가능한 7개 데이터셋 내장:
+
+```python
+from vectrix import loadSample, forecast
+
+df = loadSample("airline")       # 144개 월간 관측
+result = forecast(df, date="date", value="passengers", steps=12)
+```
+
+사용 가능: `airline`, `retail`, `stock`, `temperature`, `energy`, `web`, `intermittent`
+
 ### 전부 순수 Python
 
-위의 모든 기능 — 예측 모델, 레짐 감지, 회귀분석 진단, 제약조건 적용, 계층적 조정 — 이 NumPy, SciPy, Pandas만으로 구현되어 있습니다. 컴파일된 확장이나 시스템 의존성이 없습니다.
+위의 모든 기능 — 예측 모델, 레짐 감지, 회귀분석 진단, 제약조건 적용, 계층적 조정 — 이 NumPy, SciPy, Pandas만으로 구현되어 있습니다. Rust turbo는 선택사항이며 필수가 아닙니다.
 
 <br>
 
@@ -114,9 +140,10 @@ pip install vectrix
 ```
 
 ```python
-from vectrix import forecast
+from vectrix import forecast, loadSample
 
-result = forecast("sales.csv", steps=12)
+df = loadSample("airline")
+result = forecast(df, date="date", value="passengers", steps=12)
 print(result)
 result.plot()
 ```
@@ -128,11 +155,13 @@ result.plot()
 | | Vectrix | statsforecast | Prophet | Darts |
 |:--|:--:|:--:|:--:|:--:|
 | **순수 Python (C/Fortran 없음)** | ✅ | ❌ (numba) | ❌ (cmdstan) | ❌ (torch) |
+| **선택적 Rust 가속** | ✅ (5-10x) | ❌ | ❌ | ❌ |
 | **의존성** | 3 | 5+ | 10+ | 20+ |
 | **자동 모델 선택** | ✅ | ✅ | ❌ | ❌ |
 | **평탄 예측 방어** | ✅ | ❌ | ❌ | ❌ |
 | **비즈니스 제약조건** | 8종 | ❌ | ❌ | ❌ |
 | **내장 회귀분석** | R 스타일 | ❌ | ❌ | ❌ |
+| **샘플 데이터셋** | 7종 내장 | ❌ | ❌ | ✅ |
 
 <br>
 
@@ -224,6 +253,7 @@ result.plot()
 
 ```bash
 pip install vectrix                # 핵심 (numpy + scipy + pandas)
+pip install "vectrix[turbo]"       # + Rust 가속 (5-10배 속도 향상)
 pip install "vectrix[numba]"       # + Numba JIT (2-5배 가속)
 pip install "vectrix[ml]"          # + LightGBM, XGBoost, scikit-learn
 pip install "vectrix[all]"         # 전체
@@ -382,7 +412,11 @@ vectrix/
 ├── hierarchy/             Bottom-up, Top-down, MinTrace
 ├── intervals/             Conformal + Bootstrap 구간
 ├── ml/                    LightGBM, XGBoost 래퍼
-└── global_model/          크로스시리즈 예측
+├── global_model/          크로스시리즈 예측
+└── datasets.py            7개 내장 샘플 데이터셋
+
+rust/                         선택적 Rust 가속 (vectrix-core)
+└── src/lib.rs             ETS, ARIMA, Theta, SES 핫 루프 (PyO3)
 ```
 
 <br>

docs/getting-started/installation.ko.md

@@ -22,12 +22,31 @@
 ## 선택적 추가 기능
 
 ```bash
+pip install "vectrix[turbo]"       # Rust 가속 (5-10배 속도 향상, Rust 컴파일러 불필요)
 pip install "vectrix[numba]"       # Numba JIT 가속 (2-5배 속도 향상)
 pip install "vectrix[ml]"          # LightGBM, XGBoost, scikit-learn
+pip install "vectrix[foundation]"  # Amazon Chronos-2, Google TimesFM 2.5
 pip install "vectrix[tutorials]"   # 인터랙티브 marimo 튜토리얼
 pip install "vectrix[all]"         # 전체
 ```
 
+## Rust Turbo Mode
+
+`turbo` 옵션은 Rust로 컴파일된 네이티브 확장 `vectrix-core`를 설치합니다. 핵심 예측 루프가 5-10배 빨라집니다. 사전 빌드 wheel 제공:
+
+- Linux (x86_64, manylinux)
+- macOS (x86_64 + Apple Silicon ARM)
+- Windows (x86_64)
+- Python 3.10, 3.11, 3.12, 3.13
+
+Rust 컴파일러 불필요. 코드 변경 없이 설치만 하면 자동으로 빨라집니다.
+
+| 구성요소 | Turbo 없음 | Turbo 적용 | 속도 향상 |
+|:---------|:----------|:----------|:---------|
+| `forecast()` 200pts | 295ms | **52ms** | **5.6x** |
+| AutoETS fit | 348ms | **32ms** | **10.8x** |
+| AutoARIMA fit | 195ms | **35ms** | **5.6x** |
+
 ## 설치 확인
 
 ```python

docs/getting-started/installation.md

@@ -22,12 +22,31 @@
 ## Optional Extras
 
 ```bash
+pip install "vectrix[turbo]"       # Rust acceleration (5-10x speedup, no Rust compiler needed)
 pip install "vectrix[numba]"       # Numba JIT acceleration (2-5x speedup)
 pip install "vectrix[ml]"          # LightGBM, XGBoost, scikit-learn
+pip install "vectrix[foundation]"  # Amazon Chronos-2, Google TimesFM 2.5
 pip install "vectrix[tutorials]"   # Interactive marimo tutorials
 pip install "vectrix[all]"         # Everything
 ```
 
+## Rust Turbo Mode
+
+The `turbo` extra installs `vectrix-core`, a Rust-compiled native extension that accelerates core forecasting loops by 5-10x. Pre-built wheels are available for:
+
+- Linux (x86_64, manylinux)
+- macOS (x86_64 + Apple Silicon ARM)
+- Windows (x86_64)
+- Python 3.10, 3.11, 3.12, 3.13
+
+No Rust compiler is needed. The acceleration is transparent — your code doesn't change, it just runs faster.
+
+| Component | Without Turbo | With Turbo | Speedup |
+|:----------|:-------------|:-----------|:--------|
+| `forecast()` 200pts | 295ms | **52ms** | **5.6x** |
+| AutoETS fit | 348ms | **32ms** | **10.8x** |
+| AutoARIMA fit | 195ms | **35ms** | **5.6x** |
+
 ## Verify
 
 ```python

docs/index.md

@@ -141,6 +141,13 @@ Tested against the M3 and M4 forecasting competitions (OWA < 1.0 = beats Naive2)
     ```
     NumPy + SciPy + Pandas only. Works on Python 3.10+.
 
+=== "Rust Turbo"
+
+    ```bash
+    pip install "vectrix[turbo]"
+    ```
+    5-10x faster with Rust-accelerated core loops. No Rust compiler needed — pre-built wheels for all platforms.
+
 === "With Numba"
 
     ```bash

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vectrix"
-version = "0.0.2"
+version = "0.0.3"
 description = "Zero-config time series forecasting & analysis library. Pure numpy + scipy implementation with 30+ models, regression, and adaptive intelligence."
 readme = "README.md"
 license = {text = "MIT"}

src/vectrix/__init__.py

@@ -69,7 +69,7 @@
 )
 from .vectrix import Vectrix
 
-__version__ = "0.0.2"
+__version__ = "0.0.3"
 __all__ = [
     "Vectrix",
     "ForecastResult",

src/vectrix/vectrix.py

@@ -66,7 +66,7 @@ class Vectrix:
     Dependencies: numpy, pandas, scipy (required), numba (optional)
     """
 
-    VERSION = "0.0.2"
+    VERSION = "0.0.3"
 
     # 자체 구현 모델 목록
     NATIVE_MODELS = {