Anomaly Detection and Correlation library
The main features of linkedin/luminol are: Time Series, Time Series Analysis.
Open-source alternatives to linkedin/luminol include: nixtla/mlforecast — Scalable machine 🤖 learning for time series forecasting. nixtla/statsforecast — statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… facebook/prophet — Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict… nixtla/neuralforecast — Neuralforecast is a neural time series forecasting library designed to predict future values for one or multiple… rjt1990/pyflux — Open source time series library for Python.
Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict future values. It functions as an uncertainty estimation tool, calculating confidence intervals and error metrics to quantify the risk associated with future predictions. The project is distinguished by its ability to incorporate human-interpretable parameters for model tuning and its use of Bayesian inference for parameter estimation. It supports the integration of external regressors and special event modeling to account for the impact of holidays and specific dates on forec
Scalable machine 🤖 learning for time series forecasting.
sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran
Neuralforecast is a neural time series forecasting library designed to predict future values for one or multiple series using deep learning architectures. It functions as a distributed machine learning forecasting framework that enables the training of global models across multiple time series to improve generalization through cross-learning. The project distinguishes itself as a probabilistic forecasting toolkit that produces uncertainty intervals and probability distributions rather than single point estimates. It also includes a hierarchical forecast reconciler to ensure that predictions a