time-series

TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.

The Reporting Engine for Django. Create dashboards and standalone Reports and Charts.

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

Python implementation of soft-DTW.

DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow

Modeltime unlocks time series forecast models and machine learning in one framework

Time series analysis for Rust, with bindings to Python and Javascript

A curated collection of public industrial datasets.

Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415

Large Language & Foundation Models for Time Series.

Implementations, Pre-training Code and Datasets of Large Time-Series Models

A comprehensive survey on the time series domains

This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.

A friendly package for Kepler & TESS time series analysis in Python.

Self-supervised contrastive learning for time series via time-frequency consistency

Tutorials of econometrics featuring Python programming. This is a crash course for reviewing the most important concepts and techniques of basic econometrics, the theories are presented lightly without hustles of derivation and Python codes are straightforward.

A Python Package For System Identification Using NARMAX Models

SiriDB is a highly-scalable, robust and super fast time series database. Build from the ground up SiriDB uses a unique mechanism to operate without a global index and allows server resources to be added on the fly. SiriDB's unique query language includes dynamic grouping of time series for easy analysis over large amounts of time series.

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516