Time-based features: give the model "memory" without changing the architecture
A raw sales dataset is just numbers. The model does not know that "yesterday was 120, the day before 135". Feature engineering gives it that memory: we add new columns — lags, rolling means, differences. The model sees not only the current value but the pattern over time. That often improves accuracy more than swapping algorithms.
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