The AI framework
for Time Series
- Forecast
- Detect
- Classify
- open source
- no lock-in
- cutting edge
- user led
- sovereign
- business ready
Hourly energy demand
Forecast load to balance the grid and cut peak costs.
Daily product demand
Predict purchases per store to avoid stockouts and overstock.
Predictive maintenance
Spot anomalies in sensor data before machines fail.
Volatility & risk
Model market signals with calibrated confidence ranges.
Plan better, spend less
We implement and run sktime inside your company.
- Production-grade AI
- SLA & support
- Forward-deployed engineers
- Training & consulting
Get started in minutes
One install, one API, every model.
- Unified API
- 500+ models
- Open source
- Permissive license
Many AI models.
One harness.
Classical statistics, deep learning and modern AI foundation models. sktime gives you every approach behind a single, consistent API. Pick the right model for each task, and switch anytime without rewriting your code.
500+ models, one API
Classical statistics, machine learning, deep learning, and modern AI, all behind the same interface.
Switch models in one line
Swap algorithms to find what works best, without rewriting your pipeline or learning a new tool.
Modern AI, no lock-in
Run AI foundation models next to proven classics, same API, no lock-in. Open source, so your data stays yours.
Add private components easily
Bring your own estimators, transformers, or adapters from a closed codebase and use them through the same sktime API.
from sktime.forecasting.arima import AutoARIMA
from sktime.datasets import load_airline
y = load_airline() # your time series
forecaster = AutoARIMA(sp=12)
forecaster.fit(y) # train on history
y_pred = forecaster.predict(fh=[1, 2, 3])Swapping a model is a two-line edit. Your load, fit, and predict stay the same.
Browse all 500+ modelsOne framework,
every time series task
A unified Python API for the complete time series machine learning lifecycle, from exploration to production.
Forecasting
Predict future demand, revenue, and capacity so teams can plan ahead with confidence.
Feature Extraction
Turn raw time series into clean, model-ready inputs without slow manual preparation.
Probabilistic Prediction
Get forecasts with confidence ranges, not just single numbers, so you can weigh risk and uncertainty.
Anomaly and Change Detection
Catch equipment failures, fraud, and sudden shifts the moment they appear in your data.
Classification
Sort time series into categories automatically. Signal types, product groups, or machine states.
Time-to-Event Modeling
Estimate when an event will happen — churn, failure, or conversion — and how likely it is.
Regression
Estimate a number from a sequence, such as remaining equipment life or expected output.
Clustering
Group similar time series to reveal customer segments, product families, and hidden patterns.
Alignment
Compare and match sequences that run at different speeds or start at different times.
See what better forecasts unlock
Real problems sktime helps solve across industries.
Retail demand planning
Model demand uncertainty across products, stores, and regions so planners can size stock to risk instead of a single number.
Energy demand & load
Forecast power load with uncertainty ranges to cover the evening peak — without paying to over-provision every hour.
- Predictive maintenance
- Capacity & workforce planning
Start building in minutes
Install sktime and go from raw data to a forecast with a familiar, scikit-learn-style API.
$ pip install sktimeNew to time series?
No problem. The getting-started guide explains the core ideas and walks you through your first project step by step.
Start with the basicsFive lines from data
to prediction
sktime's unified API makes time series forecasting as simple as scikit-learn makes classification. Load data, fit a model, predict the future.
- Consistent API across 500+ models
- Swap models without changing your code
- Built on scikit-learn design patterns
1 from sktime.forecasting.chronos import ChronosForecaster
2 from sktime.datasets import load_airline
3
4 y = load_airline() # monthly passenger data
5 forecaster = ChronosForecaster(
6 "amazon/chronos-bolt-tiny"
7 ) # create forecaster
8 forecaster.fit(y) # learn from history
9 y_pred = forecaster.predict( # predict next 3 months
10 fh=[1, 2, 3]
11 )Teams already run sktime in production
Forecasting, classification, and fast prototyping - in industry and in research. Every quote below comes from the sktime user registration form.
“sktime has greatly streamlined my work developing forecasting projects. I can provide valuable results for my company in a fast and reliable way.”“sktime provides all the tools I need to create forecasts in a powerful and flexible way, from simple automated interfaces to fine-grained transformer/pipeline-based APIs, while encouraging users to follow best practices.”“It enhances my productivity. I use implemented estimators and also implement new ones rapidly to study proofs-of-concept models. The interface is general, avoiding redundant code.”“I used sktime for my cyber security research project and this library shows high accuracy in detecting and classifying threat scenarios. This library is easy to use, and the community is very supportive and approachable.”“An easy to use, extensible framework for time series tasks. Supports most time series tasks, very good for fast prototyping.”“One of the most useful libraries I use.”“Its consistent interface has streamlined the process of implementing various time series forecasting and machine learning algorithms, making it accessible and efficient.”Published with each author’s consent. Company names appear only where the author allowed it; everyone else is described by sector.
Teams already run sktime in production
Forecasting, classification, and fast prototyping - in industry and in research. Every quote below comes from the sktime user registration form.
“sktime has greatly streamlined my work developing forecasting projects. I can provide valuable results for my company in a fast and reliable way.”Forecasting
“sktime provides all the tools I need to create forecasts in a powerful and flexible way, from simple automated interfaces to fine-grained transformer/pipeline-based APIs, while encouraging users to follow best practices.”
Marc Rovira · Hierarchical forecasting, consumer goods manufacturer Prototyping
“It enhances my productivity. I use implemented estimators and also implement new ones rapidly to study proofs-of-concept models. The interface is general, avoiding redundant code.”
Felipe Angelim · Forecasting, e-commerce marketplace Classification
“I used sktime for my cyber security research project and this library shows high accuracy in detecting and classifying threat scenarios. This library is easy to use, and the community is very supportive and approachable.”
Carol Lo · Cyber security research, University of the West of England Prototyping
“An easy to use, extensible framework for time series tasks. Supports most time series tasks, very good for fast prototyping.”
Sumanta Mukherjee · Model benchmarking, enterprise technology research Forecasting
“One of the most useful libraries I use.”
David R Palmer · Quantitative research, Harvester Capital Management Prototyping
“Its consistent interface has streamlined the process of implementing various time series forecasting and machine learning algorithms, making it accessible and efficient.”
Rahul Das · Analytics and model building, IT services
Published with each author’s consent. Company names appear only where the author allowed it; everyone else is described by sector.
Teams already run sktime in production
Forecasting, classification, and fast prototyping - in industry and in research. Every quote below comes from the sktime user registration form.
“sktime has greatly streamlined my work developing forecasting projects. I can provide valuable results for my company in a fast and reliable way.”“sktime provides all the tools I need to create forecasts in a powerful and flexible way, from simple automated interfaces to fine-grained transformer/pipeline-based APIs, while encouraging users to follow best practices.”“It enhances my productivity. I use implemented estimators and also implement new ones rapidly to study proofs-of-concept models. The interface is general, avoiding redundant code.”“I used sktime for my cyber security research project and this library shows high accuracy in detecting and classifying threat scenarios. This library is easy to use, and the community is very supportive and approachable.”“An easy to use, extensible framework for time series tasks. Supports most time series tasks, very good for fast prototyping.”“One of the most useful libraries I use.”“Its consistent interface has streamlined the process of implementing various time series forecasting and machine learning algorithms, making it accessible and efficient.”Published with each author’s consent. Company names appear only where the author allowed it; everyone else is described by sector.
Built by the community, for the community
sktime is free and open source under the BSD-3-Clause license. Join hundreds of contributors shaping the future of time series in Python.
Need time series AI tailored to your business?
Custom features, infrastructure integration, and dedicated support - so you can focus on decisions, not pipelines.