Projects sharing features with Minirocket

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

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

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…

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

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

This code provides a PyTorch implementation and pretrained models for DepthContrast, as described in the paper Self-Supervised Pretraining of 3D Features on any Point-Cloud.

An intuitive library to extract features from time series.

This repository contains the official PyTorch implementation of the following paper:

Kronecker Time-Varying Graphical Lasso (KTVGL) implementation.

ICCV 2021- Oral Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Voice2Series: Reprogramming / Prompting Acoustic Models for Time Series Classification

This repo contains the source code of our ECCV 2022 paper MS-CLIP:

This is the official implementation of our paper: “Distilling Time-Series Foundation Models for Efficient Forecasting”.

Neural Rough Differential Equations for Long Time Series (ICML 2021) arXiv

A Python package for time series classification

Authors: Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar

Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression (ICDE 26)

Prathyush Parvatharaju, Ramesh Doddaiah, Tom Hartvigsen, Elke Rundensteiner

A flexible, intuitive and fast forecasting library