sktime
Unified framework for machine learning with time series data
Pricing
Free tier
Flat rate
Adoption
↗RisingLicense
Open Source
Data freshness
Verified · Jul 16, 2026Overview
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
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Does not natively support some advanced models like Long Short-Term Memory (LSTM) networks without integration with external libraries such as TensorFlow or PyTorch
Processing time increases significantly with larger time series data, leading to slower training and prediction times
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
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with sktime
Step-by-step setup guide with code examples and common gotchas.