Signal Processing
Methods for extracting meaningful information from measurement, sensor, and time-series data.
TS-Analytics develops and applies signal processing methods for technical and scientific data where raw signals need to be cleaned, transformed, characterized, and interpreted.
Methods for extracting meaningful information from measurement, sensor, and time-series data.
TS-Analytics develops and applies signal processing methods for technical and scientific data where raw signals need to be cleaned, transformed, characterized, and interpreted.
Typical project situations
Signal processing is relevant when measurement or sensor data are noisy, time-dependent, or difficult to interpret directly.
Typical situations include:
- Measurement signals require filtering, preprocessing, or quality assessment
- Relevant features need to be extracted from sensor or time-series data
- Time-series contain trends, transients, periodic components, or changing patterns
- Spectral or frequency-domain characteristics need to be evaluated
- Signal data need to be prepared for modeling, statistics, or automated workflows
What TS-Analytics can provide
Signal processing support can range from focused preprocessing tasks to complete analysis workflows for repeated measurement or sensor datasets.
Typical tasks
- Filtering and preprocessing
- Feature extraction
- Spectral and frequency-domain analysis
- Time-series characterization
- Signal visualization and quality assessment
Typical deliverables
- Signal processing workflow
- Extracted signal features and result visualizations
- Documented preprocessing and analysis scripts
Example applications
Sensor and measurement data
Processing and characterization of measurement signals from sensors, instruments, or experimental setups to extract relevant information and reduce noise.
Time-series and repeated measurements
Analysis of trends, variability, transient events, periodic structures, and changes across repeated measurements or experimental conditions.
Signal preparation for modeling
Transformation of raw signals into cleaned datasets or structured features for model-based estimation, statistical analysis, or automated evaluation.
Related services
Signal processing is often the first step in a larger analytical workflow.
Modeling & Inference →
For parameter estimation, system-state inference, inverse problems, and model-based interpretation.
Statistical Analysis →
For uncertainty evaluation, method comparison, performance assessment, and quantitative result interpretation.
Automated Workflows →
For reproducible signal processing pipelines and repeated analysis of measurement datasets.
Need Support with Signal Data?
Need support with measurement, sensor, or time-series data?
Get in touch to discuss how TS-Analytics can support signal preprocessing, feature extraction, signal characterization, or reproducible workflow development.
Free initial call · NDA possible · Remote collaboration across AT/DE/EU