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TimeGEN-1 Quickstart (Azure)

Source: vignettes/azure-quickstart.Rmd
azure-quickstart.Rmd
library(nixtlar)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) : 
#>   object 'type_sum.accel' not found

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.

  • Go to ml.azure.com
  • Sign in or create an account.
  • If you don’t have one already, create a workspace. This might require a subscription.

  • Click on Models in the sidebar and select TimeGEN in the model catalog.

  • Click Deploy. This will create an Endpoint.

  • Go to your Endpoint in the sidebar. Here you will find your Base URL and the API key.

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))
#> Frequency chosen: h
head(nixtla_client_fcst)
#>   unique_id                  ds  TimeGPT TimeGPT-lo-95 TimeGPT-lo-80
#> 1        BE 2016-12-31 00:00:00 45.19067      30.49690      35.50871
#> 2        BE 2016-12-31 01:00:00 43.24491      28.96405      35.37627
#> 3        BE 2016-12-31 02:00:00 41.95889      27.06674      35.34068
#> 4        BE 2016-12-31 03:00:00 39.79668      27.96726      32.32737
#> 5        BE 2016-12-31 04:00:00 39.20456      24.66173      30.99833
#> 6        BE 2016-12-31 05:00:00 40.10912      23.05270      32.43550
#>   TimeGPT-hi-80 TimeGPT-hi-95
#> 1      54.87264      59.88444
#> 2      51.11356      57.52577
#> 3      48.57710      56.85105
#> 4      47.26598      51.62609
#> 5      47.41079      53.74739
#> 6      47.78273      57.16553

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.