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 🚀.

Nixtla    

Nixtla

Forecast using TimeGPT

Nixtla offers a collection of classes and methods to interact with the API of TimeGPT.

🕰️ TimeGPT: Revolutionizing Time-Series Analysis

Developed by Nixtla, TimeGPT is a cutting-edge generative pre-trained transformer model dedicated to prediction tasks. 🚀 By leveraging the most extensive dataset ever – financial, weather, energy, and sales data – TimeGPT brings unparalleled time-series analysis right to your terminal! 👩‍💻👨‍💻

In seconds, TimeGPT can discern complex patterns and predict future data points, transforming the landscape of data science and predictive analytics.

⚙️ Fine-Tuning: For Precision Prediction

In addition to its core capabilities, TimeGPT supports fine-tuning, enhancing its specialization for specific prediction tasks. 🎯 This feature is like training a machine learning model on a targeted data subset to improve its task-specific performance, making TimeGPT an even more versatile tool for your predictive needs.

🔄 Nixtla: Your Gateway to TimeGPT

With Nixtla, you can easily interact with TimeGPT through simple API calls, making the power of TimeGPT readily accessible in your projects.

💻 Installation

Get Nixtla up and running with a simple pip command:

pip install nixtla>=0.4.0

🎈 Quick Start

Get started with TimeGPT now:

df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')

from nixtla import NixtlaClient
nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)
fcst_df = nixtla_client.forecast(df, h=24, level=[80, 90])