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:
Models
in the sidebar and select TimeGEN
in the model catalog.
Deploy
. This will create an Endpoint.
nixtlar
In your favorite R IDE, install nixtlar
from CRAN or GitHub.
install.packages("nixtlar") # CRAN version library(devtools) devtools::install_github("Nixtla/nixtlar")
To do this, use the nixtla_client_setup
function.
nixtla_client_setup( base_url = "Base URL here", api_key = "API key here" )
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.
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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