Nixtla / nixtla

Nixtla     TimeGPT-1 The first foundation model for forecasting and anomaly detection **TimeGPT** is a production ready, generative pretrained transformer for time series. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀. 🚀 Quick Start https://github.com/Nixtla/nixtla/assets/4086186/163ad9e6-7a16-44e1-b2e9-dab8a0b7b6b6 Install nixtla's SDK Import libraries and load data Forecast using TimeGPT in 3 easy steps Anomaly detection using TimeGPT in 3 easy steps 🤓 API support for other languages Explore our API Reference to discover how to leverage TimeGPT across various programming languages including JavaScript, Go, and more. ❄️ Snowflake Deployment Run TimeGPT directly within your Snowflake environment. The deployment script creates stored procedures and UDTFs that enable forecasting and anomaly detection on your Snowflake data without moving it outside your infrastructure. The script will guide you through setting up external access integrations, configuring your API key, and deploying the forecasting components to your specified database and schema. 🔥 Features and Capabilities • **Zero-shot Inference**: TimeGPT can generate forecasts and detect anomalies straight out of the box, requiring no prior training data. This allows for immediate deployment and quick insights from any time series data. • **Fine-tuning**: Enhance TimeGPT's capabilities by fine-tuning the model on your specific datasets, enabling the model to adapt to the nuances of your unique time series data and improving performance on tailored tasks. • **API Access**: Integrate TimeGPT seamlessly into your applications via our robust API. Upcoming support for Azure Studio will provide even more flexible integration options. Alternatively, deploy TimeGPT on your own infrastructure to maintain full control over your data and workflows. • **Add Exogenous Variables**: Incorporate additional variables that might influence your predictions to enhance forecast accuracy. (E.g. Special Dates, events or prices) • **Multiple Series Forecasting**: Simultaneously forecast multiple time series data, optimizing workflows and resources. • **Custom Loss Function**: Tailor the fine-tuning process with a custom loss function to meet specific performance metrics. • **Cross Validation**: Implement out of the box cross-validation techniques to ensure model robustness and generalizability. • **Prediction Intervals**: Provide intervals in your predictions to quantify uncertainty effectively. • **Irregular Timestamps**: Handle data with irregular timestamps, accommodating non-uniform interval series without preprocessing. 📚 Documentation with examples and use cases Dive into our comprehensive documentation to discover examples and practical use cases for TimeGPT. Our documentation covers a wide range of topics, including: • **Getting Started**: Begin with our user-friendly Quickstart Guide and learn how to set up your API key effortlessly. • **Advanced Techniques**: Master advanced forecasting methods and learn how to enhance model accuracy with our tutorials on anomaly detection, fine-tuning models using specific loss functions, and scaling computations across distributed frameworks such as Spark, Dask, and Ray. • **Specialized Topics**: Explore specialized topics like handling exogenous variables, model validation through cross-validation, and strategies for forecasting under uncertainty. • **Real World Applications**: Uncover how TimeGPT is applied in real-world scenarios through case studies on forecasting web traffic and predicting Bitcoin prices. 🗞️ TimeGPT1 Revolutionizing Forecasting and Anomaly Detection Time series data is pivotal across various sectors, including finance, healthcare, meteorology, and social sciences. Whether it's monitoring ocean tides or tracking the Dow Jones's daily closing values, time series data is crucial for forecasting and decision-making. Traditional analysis methods such as ARIMA, ETS, MSTL, Theta, CES, machine learning models like XGBoost and LightGBM, and deep learning approaches have been standard tools for analysts. However, TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity. Thanks to its zero-shot inference capability, TimeGPT streamlines the analytical process, making it accessible even to users with minimal coding experience. TimeGPT is user-friendly and low-code, enabling users to upload their time series data and either generate forecasts or detect anomalies with just a single line of code. As the only foundation model for time series analysis out of the box, TimeGPT can be integrated via our public APIs, through Azure Studio (coming soon), or deployed on your own infrastructure. ⚙️ TimeGPT's Architecture Self-attention, the revolutionary concept introduced by the paper “Attention is all you need“, is the basis of the this foundational model. The TimeGPT model is not based on any existing large language model(LLMs). It is independently trained on vast timeseries dataset as a large transformer model and is designed so as to minimize the forecasting error. The architecture consists of an encoder-decoder structure with multiple layers, each with residual connections and layer normalization. Finally, a linear layer maps the decoder’s output to the forecasting window dimension. The general intuition is that attentionbased mechanisms are able to capture the diversity of past events and correctly extrapolate potential future distributions. TimeGPT was trained on, to our knowledge, the largest collection of publicly available time series, collectively encompassing over 100 billion data points. This training set incorporates time series from a broad array of domains, including finance, economics, demographics, healthcare, weather, IoT sensor data, energy, web traffic, sales, transport, and banking. Due to this diverse set of domains, the training…