Many organizations lack the resources or expertise to build and maintain in-house machine learning teams for time series forecasting and anomaly detection. Traditional forecasting methods often require extensive data preprocessing, model selection, and hyperparameter tuning, making them inaccessible to non-experts.

Nixtla offers a suite of open-source time series forecasting tools and a foundation model, TimeGPT, trained on a large collection of business data. TimeGPT provides predictive insights and anomaly detection capabilities through a simple API call, eliminating the need for specialized machine learning knowledge or extensive model training. The platform democratizes access to state-of-the-art forecasting, enabling users to generate predictions and detect anomalies without dedicated machine learning teams. Nixtla's ecosystem includes tools for statistical forecasting, neural forecasting, and hierarchical forecasting.

TimeGPT: A pre-trained time series foundation model trained on over 100 billion data points.

Simple API for generating forecasts and detecting anomalies.

Open-source ecosystem with tools like StatsForecast, MLForecast, and NeuralForecast.

Scalable forecasting for one or millions of time series.

Tools for statistical, machine learning, and neural network-based forecasting.

Hierarchical forecasting capabilities for probabilistic forecasting.

The primary target audience includes organizations across various industries, from banks to startups, seeking accessible and scalable time series forecasting and anomaly detection solutions without requiring in-house machine learning expertise.