What it is: Unified Python library for time series machine learning with scikit-learn compatible API for forecasting, classification, and regression.

What It Does Best

Unified time series API. One consistent interface for ARIMA, Prophet, deep learning, and classical forecasting. Mix and match algorithms easily.

scikit-learn compatibility. Use familiar fit(), predict(), transform() methods. Pipelines, cross-validation, and grid search work for time series.

Beyond forecasting. Time series classification, regression, clustering, annotation. Comprehensive toolbox, not just forecasting.

Key Features

Forecasting: ARIMA, Prophet, exponential smoothing, deep learning models

Classification: Classify time series sequences

Transformation: Detrending, differencing, feature extraction

Pipelines: Build composable time series workflows

Model selection: Cross-validation for temporal data

Pricing

Free: Open source (BSD license)

Commercial: No licensing costs for any use

Cloud: Free software, works anywhere

When to Use It

✅ Need unified interface for time series ML

✅ Want scikit-learn-style API for temporal data

✅ Multiple time series algorithms to compare

✅ Need time series classification or regression

✅ Building composable time series pipelines

When NOT to Use It

❌ Simple forecasting only (Prophet simpler)

❌ Need cutting-edge deep learning (use PyTorch directly)

❌ Real-time streaming data (not optimized for it)

❌ Very long sequences (deep learning frameworks better)

❌ Just need ARIMA (statsmodels sufficient)

Common Use Cases

Demand forecasting: Sales, inventory, resource planning

Anomaly detection: Identify unusual patterns in time series

Classification: Activity recognition from sensor data

Feature engineering: Extract temporal features for ML

Model comparison: Benchmark multiple forecasting methods

sktime vs Alternatives

vs Prophet: sktime more algorithms, Prophet simpler for one method

vs statsmodels: sktime unified API, statsmodels more statistical focus

vs PyTorch: sktime higher-level, PyTorch for custom architectures

Unique Strengths

Unified interface: One API for all time series tasks

scikit-learn compatible: Familiar patterns and workflows

Composable: Build complex pipelines from simple components

Comprehensive: Forecasting, classification, transformation, annotation

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