Dynamikmerkmalen

Dynamikmerkmalen, or dynamic features, are characteristics that describe how a process changes over time or under varying conditions. Unlike static features, they capture the temporal structure, such as trends, periodicity, and transient responses, providing insight into the system's dynamics.

Common types include time-domain features (mean over a window, variance, extrema, rate of change), frequency-domain features (dominant frequencies, spectral density, spectral entropy), and time-frequency features (energy distribution across scales in wavelet or short-time Fourier analyses). In fields such as linguistics and biomechanics, dynamic features may include prosody (pitch contour, speaking rate), articulation dynamics, and gait parameters. These features help describe how behavior unfolds across a sequence rather than at a single point in time.

Extraction methods: from time series data using statistical methods, signal processing, and machine learning. Methods include windowed time-domain analysis, Fourier and wavelet analyses, Hilbert transforms, autocorrelation, and dynamic time warping; or state-space and autoregressive models. Preprocessing such as normalization and denoising is often required.

Applications span multiple domains. In engineering, they support fault diagnosis and condition monitoring. In finance, they characterize volatility dynamics. In biomechanics, they underpin gait analysis. In speech and audio processing, they assist in recognition and emotion detection. In environmental science, they help quantify variability in climate and ecological time series.

Challenges include non-stationarity, noise, sampling issues, alignment, and feature selection. Interpretability and domain-specific validation are crucial, and the choice of which dynamikmerkmale to use is often task- and domain-dependent, requiring careful evaluation.