Open-source alternatives to UnsupervisedScalableRepresentationLearningTimeSeries

Univariate GARCH models in R

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

ROCKET · MINIROCKET · HYDRA

This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers, CVPR 2022

The requirements.txt file are attached for list of packages required. Python 3.9.16 torch==2.0.0 scikitlearn==0.24.2 pywavelets==1.4.1 pandas scipy statsmodels matplotlib Bottleneck

A Python toolkit for rule-based/unsupervised anomaly detection in time series

GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph

GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n

Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It enables the prediction of future values across diverse datasets without requiring task-specific training or optimization. The system functions as a probabilistic forecasting tool, producing multiple future trajectories and quantile forecasts to quantify uncertainty and potential prediction errors. It incorporates exogenous covariate integration to merge external variables and historical context into the input stream for increased precision. The project includes utilities for sy

tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw time series data. It transforms sequential data into tabular datasets, converting time series into a flat format where each row represents a unique entity and columns represent extracted features. The project distinguishes itself through a parallel data processing framework that distributes heavy computational workloads across multiple CPU cores. It also implements hypothesis-based feature selection to identify the most predictive characteristics and filter out irrelevant ones

Paper published at ICML22. Link to our paper: https://icml.cc/virtual/2022/spotlight/18038

Time-aware tibbles

Bringing financial analysis to the tidyverse

Time series analysis in the tidyverse

A python library for time-series smoothing and outlier detection in a vectorized way.

Automatic discovery of non-trivial statistical truths from 500+ public time series — mutual information, Granger causality, FDR correction

by Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi.

:construction: WIP :construction:

This is the author code of "Learnable Dynamic Temporal Pooling for Time Series Classification" (AAAI 2021). - We employ (and customize) the fast CUDA implementation of soft-dtw (based on pytorch), publicly available at https://github.com/Maghoumi/pytorch-softdtw-cuda. - For more details of…

The Tidymodels Extension for GARCH models