caerus is Python package to compute the local-minima with the corresponding local-maxima within the given time-frame. This approach is designed to for stock-market valley and peak detection.

PyTorch Forecasting is a PyTorch-based package for forecasting with state-of-the-art deep learning architectures. It provides a high-level API and uses PyTorch Lightning to scale training on GPU or CPU, with automatic logging.

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes with time series algorithms and scikit-learn compatible tools to build, tune, and validate time series models.

This project is an sklearn extension for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and a final estimator compatible with sklearn model evaluation and parameter optimization tools. Seglearn provides a flexible approach to multivariate time series and contextual data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models.

Nolds is a small numpy-based library that provides an implementation and a learning resource for nonlinear measures for dynamical systems based on one-dimensional time series.

This package provides utilities related to the detection of peaks on 1D data. Includes functions to estimate baselines, finding the indexes of peaks in the data and performing Gaussian fitting or centroid computation to further increase the resolution of the peak detection.

This package provides a powerful and scalable library that can be used for a variety of time series data mining tasks.

MrSQM, Multiple Representations Sequence Miner is a time series classifier. The MrSQM method can quickly extract features from multiple symbolic representations of time series and train a linear classification model with logistic regression. The method has four variants with four different feature selection strategies:

• MrSQM-R: Random feature selection.

• MrSQM-RS: MrSQM-R with a follow-up Chi2 test to filter less important features.

• MrSQM-S: Pruning the all-subsequence feature space with a Chi2 bound and selecting the optimal set of top k subsequences.

• MrSQM-SR: Random sampling of the features from the output of MrSQM-S.

pyEntropy is a lightweight library built on top of NumPy that provides functions for computing various types of entropy for time series analysis.

This package provides a Pytorch/Captum/Tensorflow implementation of Cross-Domain Saliency Maps. The method does not require any model model retraining or modications.

This package provides a Python library for Empirical Mode Decomposition and related spectral analyses.

skpro is a unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in Python.

It provides scikit-learn-like, scikit-base compatible interfaces to:

• tabular supervised regressors for probabilistic prediction

• tabular probabilistic time-to-event and survival prediction

• metrics to evaluate probabilistic predictions

• reductions to turn scikit-learn regressors into probabilistic skpro regressors

• building pipelines and composite models

• symbolic probability distributions

skbase provides base classes for creating scikit-learn-like parametric objects, along with tools to make it easier to build your own packages that follow these design patterns.

findpeaks is a comprehensive Python library for robust detection and analysis of peaks and valleys in both 1D vectors and 2D arrays (images). The library provides multiple detection algorithms including topology-based persistent homology (most robust), mask-based local maximum filtering, and traditional peakdetect approaches.

This package provides high-performance algorithm implementations to build visibility graphs from time series data.

Skforecast is a Python library for time series forecasting using statistical and machine learning models. It works with any estimator compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

This library provides easy to use constructors for custom filtrations that are suitable for use with Phat. Phat currently provides a clean interface for persistence reduction algorithms for boundary matrices. This tool helps bridge the gap between data and boundary matrices. Currently, we support construction of Alpha, Rips, and Cech filtrations.

Tadasets provides various utilities for creating and loading data sets that are useful for Topological Data Analysis. Currently, we provide several synthetic data sets with particular topological features.

This package provides a library implementing the Mapper algorithm in Python. KeplerMapper can be used for visualization of high-dimensional data and 3D point cloud data. KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms.

This package provides Python bindings for PHAT, a software library which contains methods for computing the persistence pairs of a filtered cell complex represented by an ordered boundary matrix with Z2 coefficients.

Scikit-TDA is a home for Topological Data Analysis Python libraries intended for non-topologists. This project aims to provide a curated library of TDA Python tools that are widely usable and easily approachable. It is structured so that each package can stand alone or be used as part of the scikit-tda bundle.

Pytest test utilities used by DVC.