4 ms·
Even more so since test data can't be from the same time range: i.e. for time series you need to split train/test by date, not randomly, otherwise your model ju
by vl 8y ago
Even more so since test data can't be from the same time range: i.e. for time series you need to split train/test by date, not randomly, otherwise your model just memorizes the series.
- ipsa 8y agoThe article splits in time, not randomly.
- goodside 8y agoIt's standard practice to validate forecasts on non-randomized test/validation splits of the same time series, since this simulates the conditions where the model will be deployed in reality: It will know everything there is to know about the past, and it will know nothing about the future. See Hyndman's fpp2 — https://otexts.org/fpp2/accuracy.html https://otexts.org/fpp2/accuracy.html Also, his description of rolling window validation: https://robjhyndman.com/hyndsight/rolling-forecasts/ https://robjhyndman.com/hyndsight/rolling-forecasts/