time-series-analysis

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

A Library for Advanced Deep Time Series Models for General Time Series Analysis.

A unified framework for machine learning with time series

Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

The machine learning toolkit for time series analysis in Python

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

A Python package for time series classification

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

Python AutoML for Trading Systems and Sports Betting

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

Time series explainability via self-supervised model behavior consistency

APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily

SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time

Typical use cases of opensearch serverelss: search, time-series, kinesis firehose integration, securing with VPC

Google Earth Engine tool to generate multi-modal and multi-temporal datasets, including spatially and temporally aligned Sentinel-1 SAR data, Sentinel-2 multispectral data, weather and DEM-based data. A supplementary material for Paluba et al. 2024: "Identification of Optimal Sentinel-1 SAR Polarimetric Parameters for Forest Monitoring in Czechia