1. 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 45.19045 35.50842 38.47870 40.71582
#> 2 BE 2016-12-31 01:00:00 43.24445 35.37463 37.77037 39.31913
#> 3 BE 2016-12-31 02:00:00 41.95839 35.34079 37.21802 39.44543
#> 4 BE 2016-12-31 03:00:00 39.79649 32.32625 34.98683 35.96071
#> 5 BE 2016-12-31 04:00:00 39.20454 30.99895 32.74554 34.72325
#> 6 BE 2016-12-31 05:00:00 40.10878 32.43504 34.25011 35.10687
#> TimeGPT-q-40 TimeGPT-q-50 TimeGPT-q-60 TimeGPT-q-70 TimeGPT-q-80 TimeGPT-q-90
#> 1 43.92545 45.19045 46.45545 49.66508 51.90221 54.87248
#> 2 42.58400 43.24445 43.90489 47.16976 48.71852 51.11427
#> 3 40.85600 41.95839 43.06078 44.47135 46.69876 48.57599
#> 4 37.46390 39.79649 42.12907 43.63226 44.60614 47.26672
#> 5 36.01405 39.20454 42.39502 43.68583 45.66353 47.41012
#> 6 38.76697 40.10878 41.45059 45.11069 45.96745 47.782524. 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)