Open Time Series
Find the right time series resource faster.
A curated map of Python packages, datasets, tutorials, survey papers, and interview prep for forecasting, anomaly detection, feature engineering, and time-series foundation models.
Start Here
Choose the task you care about.
Use these as the fastest paths into the site instead of browsing everything at once.
I Want to Forecast
Start with forecasting libraries, benchmark datasets, and model families from classical methods to foundation models.
Go to ForecastingI Need Anomaly Detection
Browse toolkits for changepoints, monitoring, matrix profiles, outlier detection, and production-oriented anomaly workflows.
Go to Anomaly DetectionI Want Foundation Models
Compare Chronos, TimesFM, MOMENT, MOIRAI, TimeGPT, OpenLTM, and related GitHub collections.
Go to Foundation ModelsI Am Preparing for Interviews
Review core modeling concepts plus newer questions on data leakage, zero-shot forecasting, intervals, and model selection.
Go to Interview QuestionsTop Picks
Start with the pages that save the most time.
Best place to start
All-in-One & AutoML if you want a broad overview before choosing a narrower path.
Best for modern forecasting
Forecasting if you want strong practical libraries across classical, deep learning, and foundation-model tooling.
Best for keeping up
SOTA Survey Papers if you want recent summaries before diving into individual repos and papers.
Browse
Jump directly to the kind of resource you need.
Datasets
Public dataset portals, benchmark repositories, Kaggle collections, and foundation-model evaluation resources.
Kaggle Competitions
Well-known forecasting competitions and a live search entry for more recent competition-style datasets.
Books
Books that cover practical forecasting, anomaly detection, and real-world ML workflows for time series.
Courses
Free or accessible courses for Python forecasting, validation, deep learning, and hands-on modeling.
YouTube Tutorials
Playlists and long-form videos for learning concepts quickly or following code-first walkthroughs.
Feature Engineering
Feature extraction, entropy and complexity measures, smoothing, and automated feature generation libraries.
Journal and Conference
Common journals and conferences for time-series, forecasting, anomaly detection, and machine learning research.
Paths
Follow a path based on how you plan to use the site.
Beginner path
Books -> Online Courses -> Forecasting -> Public Datasets
Production path
All-in-One -> Forecasting -> Anomaly Detection -> Data Engineering
Research path
Survey Papers -> Foundation Models -> Public Datasets -> Kaggle Competitions