A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business. | It is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting and anomaly detection for both univariate and multivariate time series. |
| - | Standardized and easily extensible data loading & benchmarking for a wide range of forecasting and anomaly detection datasets;
A library of diverse models for both anomaly detection and forecasting, unified under a shared interface. Models include classic statistical methods, tree ensembles, and deep learning approaches. Advanced users may fully configure each model as desired;
Abstract DefaultDetector and DefaultForecaster models that are efficient, robustly achieve good performance, and provide a starting point for new users;
AutoML for automated hyperaparameter tuning and model selection;
Practical, industry-inspired post-processing rules for anomaly detectors that make anomaly scores more interpretable, while also reducing the number of false positives |
Statistics | |
GitHub Stars 2.7K | GitHub Stars 4.4K |
GitHub Forks 379 | GitHub Forks 349 |
Stacks 72 | Stacks 2 |
Followers 27 | Followers 4 |
Votes 0 | Votes 0 |
Integrations | |

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