Prediction Intervals • nixtlar

library(nixtlar)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) : 
#>   object 'type_sum.accel' not found

1. Uncertainty quantification via prediction intervals

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 prediction intervals with TimeGPT via the nixtlar package.

A prediction interval is a range of values that the forecast can take with a given probability, often referred to as the confidence level. Hence, a 95% prediction interval should contain a range of values that includes the actual future value with a probability of 95%. Prediction intervals are part of probabilistic forecasting, which, unlike point forecasting, aims to generate the full forecast distribution instead of just the mean or the median of that distribution.

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 35.61

3. Forecast with prediction intervals

TimeGPT can generate prediction intervals when using the following functions:

- nixtlar::nixtla_client_forecast()
- nixtlar::nixtla_client_historic() 
- nixtlar::nixtla_client_detect_anomalies()
- nixtlar::nixtla_client_cross_validation()

For any of these functions, simply set the level argument to the desired confidence level for the prediction intervals. Keep in mind that level should be a vector with numbers between 0 and 100. You can use either quantiles or level for uncertainty quantification, but not both.

fcst <- nixtla_client_forecast(df, h = 8, level=c(80,95))
#> Frequency chosen: h
head(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

Note that the level argument in the nixtlar::nixtla_client_detect_anomalies() function only uses the maximum value when multiple values are provided. Therefore, setting level = c(90, 95, 99), for example, is equivalent to setting level = c(99), which is the default value.

anomalies <- nixtla_client_detect_anomalies(df) # level=c(90,95,99)
#> Frequency chosen: h
head(anomalies) # only the 99% confidence level is used 
#>   unique_id                  ds     y anomaly  TimeGPT TimeGPT-lo-99
#> 1        BE 2016-10-27 00:00:00 52.58   FALSE 56.07256     -28.58787
#> 2        BE 2016-10-27 01:00:00 44.86   FALSE 52.41305     -32.24738
#> 3        BE 2016-10-27 02:00:00 42.31   FALSE 52.80585     -31.85458
#> 4        BE 2016-10-27 03:00:00 39.66   FALSE 52.58125     -32.07918
#> 5        BE 2016-10-27 04:00:00 38.98   FALSE 52.66716     -31.99328
#> 6        BE 2016-10-27 05:00:00 42.31   FALSE 54.10136     -30.55907
#>   TimeGPT-hi-99
#> 1      140.7330
#> 2      137.0735
#> 3      137.4663
#> 4      137.2417
#> 5      137.3276
#> 6      138.7618

4. Plot prediction intervals

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 prediction intervals.

nixtla_client_plot(df, fcst, max_insample_length = 100)

nixtlar::nixtla_client_plot(df, anomalies, plot_anomalies = TRUE)