A forecasting model can look unusually accurate when its test contains the past and its training contains the future. Randomly shuffling dated records may create exactly that arrangement. The model is being assessed on a problem that does not resemble the one it will face after deployment.
Start with the prediction moment. In a fictional shop example, the task is to forecast next week’s orders every Monday morning. The test should ask what could have been predicted that Monday, using the information actually available then. A sales correction entered on Wednesday does not belong in Monday’s inputs simply because it refers to the previous week.
Draw the timeline before selecting the splitter
Mark when the features become available, when the forecast is made and when its outcome becomes known. These can be three different dates. Check derived features too: a rolling average accidentally calculated with future rows can undermine an otherwise chronological split.
Scikit-learn’s TimeSeriesSplit documentation describes successive training sets followed by later test sets, with an optional gap. It also explains the equal-spacing assumption needed for comparable test durations. A splitter is a tool for implementing a design, not a substitute for deciding what the dates mean.
For the shop example, a gap might be needed if an outcome takes several days to settle. Its length should follow that information delay, rather than a convenient round number.
Keep preparation inside the training boundary
Fit learned preprocessing on the training portion of each evaluation. Then apply that fitted transformation to the later test portion. Looking across the complete dataset to choose features, fill missing values or tune settings can give the model information that would not have existed at the historical prediction point.
Record several forward evaluations instead of reporting only the most flattering period. Note changing demand, missing data and shifts in operating conditions. When a score drops, investigate which prediction dates failed; the average alone will not explain it.
The useful question is not merely whether the rows are sorted. It is whether the whole forecasting process could have run, with the same information, on the day being simulated.