  Learning binary classifiers from positive and unlabeled data (PUL) is vital
in many real-world applications, especially when verifying negative examples is
difficult. Despite the impressive empirical performance of recent PUL methods,
challenges like accumulated errors and increased estimation bias persist due to
the absence of negative labels. In this paper, we unveil an intriguing yet
long-overlooked observation in PUL: \textit{resampling the positive data in
each training iteration to ensure a balanced distribution between positive and
unlabeled examples results in strong early-stage performance. Furthermore,
predictive trends for positive and negative classes display distinctly
different patterns.} Specifically, the scores (output probability) of unlabeled
negative examples consistently decrease, while those of unlabeled positive
examples show largely chaotic trends. Instead of focusing on classification
within individual time frames, we innovatively adopt a holistic approach,
interpreting the scores of each example as a temporal point process (TPP). This
reformulates the core problem of PUL as recognizing trends in these scores. We
then propose a novel TPP-inspired measure for trend detection and prove its
asymptotic unbiasedness in predicting changes. Notably, our method accomplishes
PUL without requiring additional parameter tuning or prior assumptions,
offering an alternative perspective for tackling this problem. Extensive
experiments verify the superiority of our method, particularly in a highly
imbalanced real-world setting, where it achieves improvements of up to $11.3\%$
in key metrics. The code is available at
\href{https://github.com/wxr99/HolisticPU}{https://github.com/wxr99/HolisticPU}.
