TimeGEN-1 Quickstart (Azure)

library(httptest2)
.mockPaths("../tests/mocks")
start_vignette(dir = "../tests/mocks")

original_options <- options("NIXTLA_API_KEY"="dummy_api_key", digits=7)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", 
  fig.width = 7, 
  fig.height = 4
)
library(nixtlar)

TimeGEN-1 is TimeGPT optimized for Azure, Microsoft's cloud computing service. You can easily access TimeGEN via nixtlar. To do this, just follow these steps:

1. Set up a TimeGEN-1 endpoint account and generate your API key on Azure.

2. Install nixtlar

In your favorite R IDE, install nixtlar from CRAN or GitHub.

install.packages("nixtlar") # CRAN version 

library(devtools)
devtools::install_github("Nixtla/nixtlar")

3. Set up the Base URL and API key

To do this, use the nixtla_client_setup function.

nixtla_client_setup(
  base_url = "Base URL here", 
  api_key = "API key here"
)

4. Start making forecasts!

Now you can start making forecasts! We will use the electricity dataset that is included in nixtlar. This dataset contains the prices of different electricity markets.

df <- nixtlar::electricity
nixtla_client_fcst <- nixtla_client_forecast(df, h = 8, level = c(80,95))
head(nixtla_client_fcst)

We can plot the forecasts with the nixtla_client_plot function.

nixtla_client_plot(df, nixtla_client_fcst, max_insample_length = 200)

To learn more about data requirements and TimeGPT's capabilities, please read the nixtlar vignettes.

Discover the power of TimeGEN on Azure via nixtlar.

Deploying TimeGEN via nixtlar on Azure allows you to implement robust and scalable forecasting solutions. This not only simplifies the integration of advanced analytics into your workflows but also ensures that you have the power of Azure’s cutting-edge technology at your disposal through a pay-as-you-go service. To learn more, read here.

options(original_options)
end_vignette()


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nixtlar documentation built on Oct. 30, 2024, 5:07 p.m.