  In this paper we revisit the problem of decomposing a signal into a tendency
and a residual. The tendency describes an executive summary of a signal that
encapsulates its notable characteristics while disregarding seemingly random,
less interesting aspects. Building upon the Intrinsic Time Decomposition (ITD)
and information-theoretical analysis, we introduce two alternative procedures
for selecting the tendency from the ITD baselines. The first is based on the
maximum extrema prominence, namely the maximum difference between extrema
within each baseline. Specifically this method selects the tendency as the
baseline from which an ITD step would produce the largest decline of the
maximum prominence. The second method uses the rotations from the ITD and
selects the tendency as the last baseline for which the associated rotation is
statistically stationary. We delve into a comparative analysis of the
information content and interpretability of the tendencies obtained by our
proposed methods and those obtained through conventional low-pass filtering
schemes, particularly the Hodrik-Prescott (HP) filter. Our findings underscore
a fundamental distinction in the nature and interpretability of these
tendencies, highlighting their context-dependent utility with emphasis in
multi-scale signals. Through a series of real-world applications, we
demonstrate the computational robustness and practical utility of our proposed
tendencies, emphasizing their adaptability and relevance in diverse time series
contexts.
