Time-Series Anomaly Detection Comprehensive Benchmark

Collection of notebooks for time series analysis

[EMNLP 2025] CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists

[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling

LangChain 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 LangChain을 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.

LangChain, LangGraph Open Tutorial for everyone!

Implementation of Google's SELF-DISCOVER

This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive A…

[EMNLP 2025 Demo] PDF scientific paper translation with preserved formats - 基于 AI 完整保留排版的 PDF 文档全文双语翻译，支持 Google/DeepL/Ollama/OpenAI 等服务，提供 CLI/GUI/MCP/Docker/Zotero

LLMAD code

aider is AI pair programming in your terminal

Public datasets for time series anomaly detection

The tutorials for PyPOTS, guide you to model partially-observed time series datasets.

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputatio…

기후환경 문제에서의 최적 제어를 위한 소프트웨어

TSB-AD: Towards A Reliable Time-Series Anomaly Detection Benchmark

Datasets for time series forecasting

Large Language & Foundation Models for Time Series.

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

Deep Learning for Time Series Classification

Code for our NeurIPS 2019 paper "Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models"

Time-series-LLM

Channel Independent LSTF Transformers

A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron.

A comprehensive survey on the time series domains

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)