Nixtla/nixtlats

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Created over 2 years ago
Updated over 2 years ago

Nixtla/nixtlats

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statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

neuralforecast

Scalable and user friendly neural 🧠 forecasting algorithms.

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 🚀.

Jupyter Notebook

hierarchicalforecast

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

mlforecast

Scalable machine 🤖 learning for time series forecasting.

tsfeatures

Calculates various features from time series data. Python implementation of the R package tsfeatures.

Nixtla

Automated time series processing and forecasting.

transfer-learning-time-series

Transfer 🤗 Learning for Time Series Forecasting

Jupyter Notebook

datasetsforecast

Datasets for time series forecasting

Jupyter Notebook

fpp3-python

Forecasting: principles and practice in python

Jupyter Notebook

timegpt-forecaster-streamlit

TimeGPT forecaster example using streamlit

vantage

Use TimeGPT to predict cloud costs and detect anomalies.

public-slides

Nixtla Public Slides

popol-vuh

Popol Vuh: Nixtla's operating system

utilsforecast

nixtlar

R SDK for TimeGPT

m4-forecasts

ZIP version of M4 forecasts uploaded to https://github.com/Mcompetitions/M4-methods/tree/master/Point%20Forecasts.

m5-forecasts

ZIP version of dataset and forecasts uploaded to https://drive.google.com/drive/folders/1D6EWdVSaOtrP1LEFh1REjI3vej6iUS_4.

nixtla-commons

Nixtla shared assets

blog

Jupyter Notebook

docs

how-to-contribute-nixtlaverse

Instruction to contribute to the Nixtla libraries