TimeGPT is a production-ready generative pretrained transformer model specifically designed for time series forecasting. It accurately forecasts domains such as retail, electricity, finance, and IoT with minimal code. Below you’ll find a high-level overview of its features, architecture, and practical examples.


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1. Start Now

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2. Choose Your Interface

You can access TimeGPT through:

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3. Upload Time Series Data

Provide your time series data. TimeGPT can consume data with irregular timestamps, exogenous variables, or multiple time series simultaneously.

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4. Forecast or Detect Anomalies

Perform zero-shot inference out-of-the-box to forecast future values or detect anomalies. Fine-tune the model if you need more targeted performance.

For detailed instructions and advanced configurations, visit our Quickstart Guide and additional tutorials.

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Features and Capabilities

Get started quickly with the Quickstart guide. Explore in-depth tutorials on TimeGPT capabilities and real-world applications.

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Architecture

TimeGPT Architecture Overview

TimeGPT’s architecture builds on the self-attention mechanism introduced in the original “Attention is All You Need” paper. Unlike typical large language models (LLMs), TimeGPT is independently trained on extensive time series datasets to minimize forecasting errors.

TimeGPT employs an encoder-decoder structure with residual connections, layer normalization, and a linear output layer to match the decoder outputs to forecast dimensions. The attention-based mechanisms help the model capture diverse historical patterns to create accurate future predictions.

The model processes input sequences from left to right, similar to how humans read sentences, and predicts future values (“tokens”) based on historical windows of time series data.

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Explore Examples and Use Cases

With TimeGPT, you can rapidly iterate from initial exploration to high-accuracy forecasting. Dive deeper into the comprehensive tutorials for more sophisticated workflows.