Building Time-Series Machine Learning Models with sktime in Python

2026-06-16 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

The sktime Python library offers a scikit-learn-style API for building time-series machine learning models, including forecasting, classification, regression, and clustering. This article demonstrates its core features through an example of forecasting hourly temperature readings from an industrial HVAC sensor, starting January 1, 2026. It covers handling time series data structures like Series, Panel, and Hierarchical, and chronologically splitting data using `temporal_train_test_split` for a 168-hour test set. The tutorial details constructing preprocessing pipelines with `TransformedTargetForecaster` to impute missing values, detrend, and deseasonalize data (with `sp=24`), before applying forecasters like `ExponentialSmoothing` or `ARIMA(1,1,1)`. Forecast evaluation uses MAE and MAPE, and the article shows how to perform robust cross-validation with `ExpandingWindowSplitter`.

Key takeaway

For Machine Learning Engineers and Data Scientists building time-series forecasting systems, `sktime` offers a standardized, scikit-learn-compatible framework to streamline development. You should consider integrating `sktime` to simplify complex time-series workflows, especially when dealing with data preparation, diverse models, and rigorous evaluation. It reduces boilerplate code and promotes modularity, making experimentation and deployment more efficient for temporal data problems.

Key insights

sktime unifies time-series ML tasks with a scikit-learn-like API, simplifying complex workflows.

Principles

• Time series data requires chronological splitting.
• Preprocessing pipelines enhance forecaster robustness.
• Consistent APIs simplify model experimentation.

Method

Split time series chronologically, define a `ForecastingHorizon`, chain transformations (imputation, detrending, deseasonalization) with a forecaster using `TransformedTargetForecaster`, then evaluate and cross-validate.

In practice

• Use `sktime` for unified time-series ML.
• Apply `TransformedTargetForecaster` for robust pipelines.
• Employ `ExpandingWindowSplitter` for reliable CV.

Topics

• sktime
• Time Series Forecasting
• Machine Learning Pipelines
• Python Libraries
• Data Preprocessing
• Model Evaluation
• Cross-Validation

Code references

• balapriyac/data-science-tutorials

Best for: Machine Learning Engineer, Data Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.