About TimeGPT

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.

It is user-friendly and low-code. Users can simply upload their time series data and generate forecasts or detect anomalies with just a single line of code.

TimeGPT is the only out of-the-box foundation model for time series that can be used through our public APIs, through Azure Studio as TimeGEN-1 or on your own infrastructure.

Get started! Activate your free trial and see our Quickstart Guide.

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 (obtain an API key through our Dashboard). TimeGPT is also supported through Azure Studio for 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.
Specific Loss Function: Tailor the fine-tuning process by choosing from many loss functions 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.
Anomaly Detection: Automatically detect anomalies in time series, and use exogenous features for enhanced performance.

Get started with our QuickStart guide, walk through tutorials on the different capabilities, and learn from real-world use cases in our documentation.

Architecture

Self-attention, the revolutionary concept introduced by the paper Attention is all you need, is the basis of this foundation model. TimeGPT model is not based on any existing large language model(LLM). Instead, it is independently trained on a vast amount of time series data, and the large transformer model is designed 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 attention-based mechanisms are able to capture the diversity of past events and correctly extrapolate potential future distributions.

To make prediction, TimeGPT “reads” the input series much like the way humans read a sentence – from left to right. It looks at windows of past data, which we can think of as “tokens”, and predicts what comes next. This prediction is based on patterns the model identifies in past data and extrapolates into the future.

Explore examples and use cases

Visit our comprehensive documentation to explore a wide range of examples and practical use cases for TimeGPT. Whether you’re getting started with our Quickstart Guide, setting up your API key, or looking for advanced forecasting techniques, our resources are designed to guide you through every step of the process.

Learn how to handle anomaly detection, fine-tune models with specific loss functions, and scale your computing using frameworks like Spark, Dask, and Ray.

Additionally, our documentation covers specialized topics such as handling exogenous variables, validating models through cross-validation, and forecasting under uncertainty with quantile forecasts and prediction intervals.

For those interested in real-world applications, discover how TimeGPT can be used for forecasting web traffic or predicting Bitcoin prices.