Cross-Validation

1. Time series cross-validation

Cross-validation is a method for evaluating the performance of a forecasting model. Given a time series, it is carried out by defining a sliding window across the historical data and then predicting the period following it. The accuracy of the model is computed by averaging the accuracy across all the cross-validation windows. This method results in a better estimation of the model’s predictive abilities, since it considers multiple periods instead of just one, while respecting the sequential nature of the data.

TimeGPT has a method for performing time series cross-validation, 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. Perform time series cross-validation

To perform time series cross-validation using TimeGPT, use nixtlar::nixtla_client_cross_validation. The key parameters of this method are:

• df: The time series data, provided as a data frame, tibble, or tsibble. It must include at least two columns: one for the timestamps and one for the observations. The default names for these columns are ds and y. If your column names are different, specify them with time_col and target_col, respectively. If you are working with multiple series, you must also include a column with unique identifiers. The default name for this column is unique_id; if different, specify it with id_col.
• h: The forecast horizon.
• n_windows: The number of windows to evaluate. Default value is 1.
• step_size: The gap between each cross-validation window. Default value is NULL.

nixtla_client_cv <- nixtla_client_cross_validation(df, h = 8, n_windows = 5)
#> Frequency chosen: h
head(nixtla_client_cv)
#>   unique_id                  ds              cutoff     y  TimeGPT
#> 1        BE 2016-12-29 08:00:00 2016-12-29 07:00:00 53.30 51.79829
#> 2        BE 2016-12-29 09:00:00 2016-12-29 07:00:00 53.93 55.48120
#> 3        BE 2016-12-29 10:00:00 2016-12-29 07:00:00 56.63 55.86470
#> 4        BE 2016-12-29 11:00:00 2016-12-29 07:00:00 55.66 54.45249
#> 5        BE 2016-12-29 12:00:00 2016-12-29 07:00:00 48.00 54.76038
#> 6        BE 2016-12-29 13:00:00 2016-12-29 07:00:00 46.53 53.56611

4. Plot cross-validation results

nixtlar includes a function to plot the historical data and any output from nixtlar::nixtla_client_forecast, nixtlar::nixtla_client_historic, nixtlar::nixtla_client_anomaly_detection 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 using nixtlar::nixtla_client_plot with the output of nixtlar::nixtla_client_cross_validation, each cross-validation window is visually represented with vertical dashed lines. For any given pair of these lines, the data before the first line forms the training set. This set is then used to forecast the data between the two lines.

nixtla_client_plot(df, nixtla_client_cv, max_insample_length = 200)