> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla.io/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# TimeGPT in R

> Using TimeGPT for time series forecasting in the R programming language

<div className="flex justify-center">
  <img src="https://github.com/Nixtla/nixtla/blob/main/nbs/img/logo_nixtlar.png?raw=true" alt="Logo for nixtlar" width="150" />
</div>

## Introduction

**TimeGPT-1**: The first foundation model for time series forecasting and anomaly detection.

The `nixtlar` package is the R interface to TimeGPT, allowing you to perform state-of-the-art time series forecasting directly from R. TimeGPT is a production-ready, generative pretrained transformer for time series forecasting, developed by Nixtla. It is capable of accurately predicting various domains such as retail, electricity, finance, and IoT, with just a few lines of code. Additionally, it can detect anomalies in time series data.

<Note>
  Version 0.6.2 of nixtlar is now available on CRAN! This version introduces support for TimeGEN-1, TimeGPT optimized for Azure, along with enhanced date support, business-day frequency inference, and various bug fixes.
</Note>

## How to use

To learn how to use `nixtlar`, please refer to the
[documentation](https://nixtla.github.io/nixtlar/).

To view directly on CRAN, please use this
[link](https://cloud.r-project.org/web/packages/nixtlar/index.html).

<Note title="API key required">
  The `nixtlar` package requires an API key. Get yours on the [Nixtla Dashboard](https://nixtla.io/free-trial?utm_source=nixtla.io\&utm_campaign=/docs/reference/timegpt_in_r).
</Note>

## Installation

```r theme={null}
# Install nixtlar from CRAN
install.packages("nixtlar")

# Then load it
library(nixtlar)

# Set your API key
nixtla_set_api_key(api_key = "Your API key here")
```

## Quick Example

```r theme={null}
# Load sample data
df <- nixtlar::electricity
head(df)

# Forecast the next 8 steps ahead
nixtla_client_fcst <- nixtla_client_forecast(df, h = 8, level = c(80,95))

# Optionally, plot the results
nixtla_client_plot(df, nixtla_client_fcst, max_insample_length = 200)
```

## Anomaly Detection Example

```r theme={null}
# Detect anomalies
nixtla_client_anomalies <- nixtlar::nixtla_client_detect_anomalies(df)

# Plot with anomalies highlighted
nixtlar::nixtla_client_plot(df, nixtla_client_anomalies, plot_anomalies = TRUE)
```

## Features and Capabilities

TimeGPT through the `nixtlar` package provides:

* **Zero-shot Inference**: Generate forecasts and detect anomalies with no prior training
* **Fine-tuning**: Enhance model performance for your specific datasets
* **Add Exogenous Variables**: Incorporate additional variables like special dates or events to improve accuracy
* **Multiple Series Forecasting**: Simultaneously forecast multiple time series
* **Custom Loss Function**: Tailor the fine-tuning process with specific performance metrics
* **Cross Validation**: Implement out-of-the-box validation techniques
* **Prediction Intervals**: Quantify uncertainty in your predictions
* **Irregular Timestamps**: Handle data with non-uniform intervals

## How to Cite

If you find TimeGPT useful for your research, please consider citing:

```
Garza, A., Challu, C., & Mergenthaler-Canseco, M. (2024). TimeGPT-1.
arXiv preprint arXiv:2310.03589. Available at
https://arxiv.org/abs/2310.03589
```

## Support

If you have questions or need support, please email `support@nixtla.io`.

TimeGPT is closed source. However, this SDK is open source and available under the Apache 2.0 License.