  Time-fluctuating signals are ubiquitous and diverse in many physical,
chemical, and biological systems, among which random telegraph signals (RTSs)
refer to a series of instantaneous switching events between two discrete levels
from single-particle movements. Reliable RTS analyses are crucial prerequisite
to identify underlying mechanisms related to performance sensitivity. When
numerous levels partake, complex patterns of multilevel RTSs occur, making
their quantitative analysis exponentially difficult, hereby systematic
approaches are found elusive. Here, we present a three-step analysis protocol
via progressive knowledge-transfer, where the outputs of early step are passed
onto a subsequent step. Especially, to quantify complex RTSs, we build three
deep neural network architectures that can process temporal data well and
demonstrate the model accuracy extensively with a large dataset of different
RTS types affected by controlling background noise size. Our protocol offers
structured schemes to quantify complex RTSs from which meaningful
interpretation and inference can ensue.
