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Top repositories

1

statsforecast

Lightning ⚑️ fast forecasting with statistical and econometric models.
Python
3,665
star
2

neuralforecast

Scalable and user friendly neural 🧠 forecasting algorithms.
Python
2,415
star
3

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 πŸš€.
Jupyter Notebook
1,901
star
4

hierarchicalforecast

Probabilistic Hierarchical forecasting πŸ‘‘ with statistical and econometric methods.
Python
533
star
5

mlforecast

Scalable machine πŸ€– learning for time series forecasting.
Python
501
star
6

tsfeatures

Calculates various features from time series data. Python implementation of the R package tsfeatures.
Python
321
star
7

Nixtla

Automated time series processing and forecasting.
Python
253
star
8

transfer-learning-time-series

Transfer πŸ€— Learning for Time Series Forecasting
Jupyter Notebook
123
star
9

datasetsforecast

Datasets for time series forecasting
Jupyter Notebook
38
star
10

fpp3-python

Forecasting: principles and practice in python
Jupyter Notebook
13
star
11

timegpt-forecaster-streamlit

TimeGPT forecaster example using streamlit
Python
12
star
12

vantage

Use TimeGPT to predict cloud costs and detect anomalies.
Python
10
star
13

nixtlats

6
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14

popol-vuh

Popol Vuh: Nixtla's operating system
Python
6
star
15

public-slides

Nixtla Public Slides
Python
5
star
16

utilsforecast

Python
4
star
17

nixtlar

R SDK for TimeGPT
R
3
star
18

m4-forecasts

ZIP version of M4 forecasts uploaded to https://github.com/Mcompetitions/M4-methods/tree/master/Point%20Forecasts.
2
star
19

m5-forecasts

ZIP version of dataset and forecasts uploaded to https://drive.google.com/drive/folders/1D6EWdVSaOtrP1LEFh1REjI3vej6iUS_4.
2
star
20

nixtla-commons

Nixtla shared assets
CSS
2
star
21

blog

Jupyter Notebook
1
star
22

docs

MDX
1
star
23

how-to-contribute-nixtlaverse

Instruction to contribute to the Nixtla libraries
1
star