sktime

Unified framework for machine learning with time series data

EstablishedOpen SourceLow lock-in

Pricing

See website

Flat rate

Adoption

Stable

License

Open Source

Data freshness

Overview

What is sktime?

A unified framework for machine learning with time series that provides a wide range of algorithms and tools to handle various time series tasks, making it easier for developers and researchers to work with time-series data.

Key differentiator

sktime stands out by providing a unified and consistent interface for various time series tasks, making it easier to integrate different algorithms and preprocessing steps into a single pipeline.

Capability profile

Strength Radar

Unified interfac…Supports a wide …Flexible pipelin…Integration with…

Honest assessment

Strengths & Weaknesses

↑ Strengths

Unified interface for time series forecasting, classification, and regression

Supports a wide range of algorithms including classical statistical models and machine learning methods

Flexible pipeline design to combine different preprocessing steps and models

Integration with scikit-learn for consistent API usage

Fit analysis

Who is it for?

✓ Best for

Data scientists working with time-series data who need a unified framework for various tasks like forecasting, classification, and regression.

Developers building applications that require handling and analyzing time series data efficiently.

✕ Not a fit for

Projects requiring real-time processing of time series data as sktime is designed more for batch processing

Applications where the primary focus is on non-time-series machine learning tasks

Cost structure

Pricing

Free Tier

None

Starts at

See website

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Next step

Get Started with sktime

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →