library(nixtlar)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
#> object 'type_sum.accel' not found1. Uncertainty quantification via quantiles
For uncertainty quantification, TimeGPT can generate
both prediction intervals and quantiles, offering a measure of the range
of potential outcomes rather than just a single point forecast. In
real-life scenarios, forecasting often requires considering multiple
alternatives, not just one prediction. This vignette will explain how to
use quantiles with TimeGPT via the nixtlar
package.
Quantiles represent the cumulative proportion of the forecast
distribution. For instance, the 90th quantile is the value below which
90% of the data points are expected to fall. Notably, the 50th quantile
corresponds to the median forecast value provided by
TimeGPT. The quantiles are produced using conformal
prediction, a framework for creating distribution-free uncertainty
intervals for predictive models.
This vignette assumes you have already set up your API key. If you haven’t done this, please read the Get Started vignette first.
2. Load data
For this vignette, we will use the electricity consumption dataset
that is included in nixtlar, which contains the hourly
prices of five different electricity markets.
df <- nixtlar::electricity
head(df)
#> unique_id ds y
#> 1 BE 2016-10-22 00:00:00 70.00
#> 2 BE 2016-10-22 01:00:00 37.10
#> 3 BE 2016-10-22 02:00:00 37.10
#> 4 BE 2016-10-22 03:00:00 44.75
#> 5 BE 2016-10-22 04:00:00 37.10
#> 6 BE 2016-10-22 05:00:00 35.613. Forecast with quantiles
TimeGPT can generate quantiles when using the following
functions:
- nixtlar::nixtla_client_forecast()
- nixtlar::nixtla_client_historic()
- nixtlar::nixtla_client_cross_validation()For any of these functions, simply set the quantiles
argument to the desired values as a vector. Keep in mind that quantiles
should all be numbers between 0 and 1. You can use either
quantiles or level for uncertainty
quantification, but not both.
fcst <- nixtla_client_forecast(df, h = 8, quantiles = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9))
#> Frequency chosen: h
head(fcst)
#> unique_id ds TimeGPT TimeGPT-q-10 TimeGPT-q-20 TimeGPT-q-30
#> 1 BE 2016-12-31 00:00:00 45.19067 35.50871 38.47798 40.71659
#> 2 BE 2016-12-31 01:00:00 43.24491 35.37627 37.77041 39.32019
#> 3 BE 2016-12-31 02:00:00 41.95889 35.34068 37.21913 39.44595
#> 4 BE 2016-12-31 03:00:00 39.79668 32.32737 34.98781 35.96059
#> 5 BE 2016-12-31 04:00:00 39.20456 30.99833 32.74744 34.72205
#> 6 BE 2016-12-31 05:00:00 40.10912 32.43550 34.24884 35.10877
#> TimeGPT-q-40 TimeGPT-q-50 TimeGPT-q-60 TimeGPT-q-70 TimeGPT-q-80 TimeGPT-q-90
#> 1 43.92536 45.19067 46.45599 49.66475 51.90336 54.87264
#> 2 42.58577 43.24491 43.90405 47.16964 48.71941 51.11356
#> 3 40.85597 41.95889 43.06182 44.47183 46.69866 48.57710
#> 4 37.46490 39.79668 42.12846 43.63277 44.60554 47.26598
#> 5 36.01383 39.20456 42.39529 43.68706 45.66167 47.41079
#> 6 38.76737 40.10912 41.45086 45.10946 45.96939 47.782734. Plot quantiles
nixtlar includes a function to plot the historical data
and any output from nixtlar::nixtla_client_forecast,
nixtlar::nixtla_client_historic,
nixtlar::nixtla_client_detect_anomalies and
nixtlar::nixtla_client_cross_validation. If you have long
series, you can use max_insample_length to only plot the
last N historical values (the forecast will always be plotted in
full).
When available, nixtlar::nixtla_client_plot will
automatically plot the quantiles.
nixtla_client_plot(df, fcst, max_insample_length = 100)