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.
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.
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.
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.