Nixtla / nixtla
TimeGPT-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 π.
β2,639Updated this week
Alternatives and similar repositories for nixtla:
Users that are interested in nixtla are comparing it to the libraries listed below
- Scalable and user friendly neural forecasting algorithms.β3,379Updated this week
- Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingβ1,399Updated last month
- Unified Training of Universal Time Series Forecasting Transformersβ1,057Updated last month
- Lightning β‘οΈ fast forecasting with statistical and econometric models.β4,191Updated this week
- Scalable machine π€ learning for time series forecasting.β990Updated last week
- Chronos: Pretrained Models for Probabilistic Time Series Forecastingβ3,076Updated 2 weeks ago
- Foundation Models for Time Seriesβ528Updated this week
- Automated Time Series Forecastingβ1,223Updated 2 weeks ago
- A toolkit for machine learning from time seriesβ1,125Updated this week
- [ICLR 2024] Official implementation of " π¦ Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"β1,859Updated 4 months ago
- Collection of notebooks for time series analysisβ389Updated 2 months ago
- β749Updated 6 months ago
- Probabilistic Hierarchical forecasting π with statistical and econometric methods.β633Updated this week
- Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.β1,085Updated 8 months ago
- TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecastβ¦β4,480Updated 2 weeks ago
- Time series forecasting with PyTorchβ4,194Updated this week
- Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).β2,151Updated this week
- A unified multi-task time series model.β515Updated 5 months ago
- MOMENT: A Family of Open Time-series Foundation Modelsβ472Updated this week
- An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arβ¦β1,834Updated 7 months ago
- Transfer π€ Learning for Time Series Forecastingβ244Updated 3 months ago
- Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024)β551Updated last week
- A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neurβ¦β1,313Updated this week
- [ICLR 2025 Spotlight] Official implementation of "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts"β500Updated this week
- Large Language & Foundation Models for Time Series.β495Updated 7 months ago
- Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal β¦β839Updated last year
- Resources about time series forecasting and deep learning.β633Updated this week
- Time series forecasting with machine learning modelsβ1,275Updated this week
- [AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"β2,140Updated last year
- A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.β1,017Updated 3 months ago