Historical Forecast

1. TimeGPT Historical Forecast

When generating a forecast, sometimes you might be interested in forecasting the historical observations. These predictions, known as fitted values, can help you better understand and evaluate a model’s performance over time.

TimeGPT has a method for generating fitted values, and users can call it from nixtlar. This vignette will explain how to do this. It 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’ll 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 historical data

To generate a forecast for the historical data, use nixtlar::nixtla_client_historic, which should include the following parameters:

• df: The time series data, either as a data frame, a tibble, or a tsibble. It should include at least a column with the datestamps and a column with the observations. Default names for these columns are ds and y. If different, please specify their names. If working with multiple series, you also need to include a column with unique identifiers. The default name for this column is unique_id.
• level: The prediction intervals for the forecast.

nixtla_client_fitted_values <- nixtla_client_historic(df, level = c(80,95))
#> Frequency chosen: h
head(nixtla_client_fitted_values)
#>                    ds  TimeGPT TimeGPT-lo-80 TimeGPT-lo-95 TimeGPT-hi-80
#> 1 2016-10-27 00:00:00 56.07623      25.27245      8.965920      86.88000
#> 2 2016-10-27 01:00:00 52.41973      21.61596      5.309425      83.22350
#> 3 2016-10-27 02:00:00 52.81474      22.01096      5.704433      83.61852
#> 4 2016-10-27 03:00:00 52.59026      21.78649      5.479953      83.39404
#> 5 2016-10-27 04:00:00 52.67297      21.86920      5.562667      83.47675
#> 6 2016-10-27 05:00:00 54.10659      23.30282      6.996284      84.91036
#>   TimeGPT-hi-95
#> 1     103.18653
#> 2      99.53004
#> 3      99.92505
#> 4      99.70057
#> 5      99.78328
#> 6     101.21690

Notice that there are no fitted values for some of the initial observations. This is because TimeGPT requires a minimum number of values to generate reliable forecasts.

All the fitted values are generated using a rolling window, meaning that the fitted value for observation \(T\) is generated using the first \(T-1\) observations.