• Stars
    star
    13
  • Rank 1,512,713 (Top 30 %)
  • Language
    Jupyter Notebook
  • License
    Apache License 2.0
  • Created about 2 years ago
  • Updated over 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Forecasting: principles and practice in python

More Repositories

1

statsforecast

Lightning ⚑️ fast forecasting with statistical and econometric models.
Python
3,846
star
2

neuralforecast

Scalable and user friendly neural 🧠 forecasting algorithms.
Python
3,001
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
2,208
star
4

hierarchicalforecast

Probabilistic Hierarchical forecasting πŸ‘‘ with statistical and econometric methods.
Python
568
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
362
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

timegpt-forecaster-streamlit

TimeGPT forecaster example using streamlit
Python
12
star
11

vantage

Use TimeGPT to predict cloud costs and detect anomalies.
Python
11
star
12

public-slides

Nixtla Public Slides
Python
6
star
13

nixtlats

6
star
14

popol-vuh

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

utilsforecast

Python
4
star
16

nixtlar

R SDK for TimeGPT
R
3
star
17

m4-forecasts

ZIP version of M4 forecasts uploaded to https://github.com/Mcompetitions/M4-methods/tree/master/Point%20Forecasts.
2
star
18

m5-forecasts

ZIP version of dataset and forecasts uploaded to https://drive.google.com/drive/folders/1D6EWdVSaOtrP1LEFh1REjI3vej6iUS_4.
2
star
19

nixtla-commons

Nixtla shared assets
CSS
2
star
20

blog

Jupyter Notebook
1
star
21

docs

MDX
1
star
22

how-to-contribute-nixtlaverse

Instruction to contribute to the Nixtla libraries
1
star