Chronos: Pretrained Models for Time Series Forecasting
Probabilistic time series modeling in Python
TimeGPT-2.1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
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
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
List of papers, code and experiments using deep learning for time series forecasting
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight)
[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Unified Training of Universal Time Series Forecasting Transformers
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
An Awesome List of the latest time series papers and code from top AI venues.
Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python
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