Kuidas jaotada andmed partiidesse statistika hindamiseks, arvestades hooajalisust ja koormust?

When distributing data into batches for statistical evaluation considering seasonality and load, it is important to preserve the temporal structure and evenly cover all seasonal patterns.

• Seasonality: Batches should include data from different seasonal periods (e.g., different days of the week, hours, months) so that the statistics reflect the variations. A sliding window or stratified sampling by time can be used.

• Load: If the load changes over time, its distribution should be taken into account so that batches are not biased by the intensity of events.

• Example approach: divide data into time intervals (e.g., by hours or days), then form batches that contain data from different time segments to cover all seasons and load levels.

• Optimization: fixed-size batches with uniform distribution over time or dynamic batches that adapt to the current load can be used.

This approach allows obtaining statistics that are robust to seasonal fluctuations and load changes, which is important for accurate model evaluation and training.