# sktime — Unified Framework for Time Series Machine Learning

> A scikit-learn compatible Python framework providing a unified interface for time series forecasting, classification, regression, clustering, and anomaly detection.

## Install

Save in your project root:

# sktime — Unified Framework for Time Series Machine Learning

## Quick Use
```bash
pip install sktime
```
```python
from sktime.forecasting.naive import NaiveForecaster
forecaster = NaiveForecaster(strategy="last")
forecaster.fit(y_train)
y_pred = forecaster.predict(fh=[1, 2, 3])
```

## Introduction
sktime is an open-source Python library that extends the scikit-learn API to time series data. It provides a unified interface for multiple time series learning tasks, making it straightforward to apply, compare, and compose algorithms across forecasting, classification, regression, clustering, and annotation.

## What sktime Does
- Provides scikit-learn-compatible estimators for time series forecasting, classification, and clustering
- Supports pipeline composition with transformers, feature extractors, and reducers
- Implements model selection tools including cross-validation strategies for temporal data
- Offers adapters to integrate algorithms from statsmodels, Prophet, and other libraries
- Includes benchmark datasets and evaluation utilities for reproducible experiments

## Architecture Overview
sktime is built around a base class hierarchy that mirrors scikit-learn's estimator pattern. Each task type (forecaster, classifier, transformer) implements `fit`, `predict`, and `transform` methods with type-checked inputs. Pipelines chain transformers and estimators, while reduction strategies convert between task types (e.g., forecasting via regression). The framework uses a tagged object system for capability discovery.

## Self-Hosting & Configuration
- Install via `pip install sktime` or `conda install -c conda-forge sktime`
- Use extras for optional dependencies: `pip install sktime[all_extras]`
- Configure forecasters with `fh` (forecasting horizon) as integer, list, or `ForecastingHorizon` object
- Set up cross-validation with `SlidingWindowSplitter` or `ExpandingWindowSplitter`
- Integrate with MLflow or other tracking tools using standard scikit-learn patterns

## Key Features
- Consistent API across all time series tasks following scikit-learn conventions
- Composable pipelines with time series-specific transformers and reducers
- Extensive algorithm library covering classical, ML, and deep learning methods
- Built-in temporal cross-validation and performance metrics
- Active community with regular releases and comprehensive documentation

## Comparison with Similar Tools
- **Darts** — Focused on forecasting with deep learning; sktime covers more task types
- **tsfresh** — Specializes in feature extraction; sktime provides a full ML pipeline
- **statsmodels** — Statistical models only; sktime wraps statsmodels and adds ML workflows
- **scikit-learn** — General ML; sktime adds time series-specific data types and methods

## FAQ
**Q: Is sktime compatible with scikit-learn?**
A: Yes. sktime follows scikit-learn's estimator interface, so pipelines, grid search, and cross-validation patterns work similarly.

**Q: Can I use deep learning models with sktime?**
A: Yes. sktime integrates with PyTorch and TensorFlow through adapter classes and supports neural network forecasters.

**Q: How does sktime handle panel (multi-instance) data?**
A: sktime uses a multi-index pandas DataFrame format for panel data, with instance and time indices.

**Q: What license does sktime use?**
A: sktime is released under the BSD 3-Clause license.

## Sources
- https://github.com/sktime/sktime
- https://www.sktime.net/en/stable/

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Source: https://tokrepo.com/en/workflows/asset-8a1fd3aa
Author: AI Open Source