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TimeGPT in R - TimeGPT Foundational model for time series forecasting and anomaly detection

Using TimeGPT for time series forecasting in the R programming language

IntroductionTimeGPT-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.

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.

How to useTo learn how to use nixtlar, please refer to the documentation. To view directly on CRAN, please use this link. Installation

# 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

# 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

# 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 CapabilitiesTimeGPT 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 CiteIf 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

SupportIf 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.

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