Nixtla is an open-source time-series startup that aims to democratize the use of state-of-the-art models in forecasting, leveraging deep learning and efficient statistical models for distributed computing using Ray. Businesses across various industries collect time-series data and seek accurate predictions, with applications ranging from manufacturing and music streaming to supply chain optimization and electricity market prediction. Nixtla's StatsForecast library efficiently addresses the challenge of predicting millions of time series using AutoARIMA, a widely used model that leverages autocorrelations for accurate predictions. By compiling AutoARIMA just-in-time using Numba and distributing computation across multiple cores with Ray, StatsForecast achieves exceptional speed and scalability, enabling data scientists to fit models in under an hour and reducing costs on AWS. The library facilitates tasks such as hyperparameter tuning, model selection, and parallelization, making it easier for users to work with time-series data. With its Ray integration, StatsForecast empowers developers to train more models and scale their forecasting capabilities, providing a powerful tool for businesses seeking efficient time-series forecasting solutions.