Financial, Security, and Digital Systems: Finance, Data Security, Signal Processing, and Anomaly Detection
Language: English
Published by Independently published, 2026
Series: Book 5 of 5 - Wavelet Transform in Practice: From Theory to Production-Ready Python Applications
- Softcover
- New
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- Title
- Financial, Security, and Digital Systems: Finance, Data Security, Signal Processing, and Anomaly Detection
- Author
- Wei, Shouke
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798251616651
- Series
- Book 5 of 5: Wavelet Transform in Practice: From Theory to Production-Ready Python Applications
The chapters in this volume examine several domains in which multiscale analysis provides meaningful analytical insight. These include wavelet-based approaches to data security and steganography, where transform-domain representations enable robust information embedding and detection; financial time-series analysis, where wavelet decompositions help reveal volatility dynamics and cross-scale interactions in market behavior; digital audio signal analysis, where wavelets support efficient representation, denoising, and feature extraction; and multiscale anomaly detection, where wavelet-based features assist in identifying abnormal events within complex systems.
A recurring theme throughout this volume is the relationship between multiscale representation and interpretability. Signals generated by financial markets, communication systems, and digital media frequently exhibit behaviors that manifest differently across time scales. Wavelet transforms provide a means of separating these scales, but meaningful interpretation requires careful consideration of system context, domain knowledge, and the mechanisms that generate the observed data. For this reason, the emphasis in this volume extends beyond algorithmic implementation to include analytical reasoning and system-aware interpretation.
Consistent with the philosophy of the series, all examples are implemented using reproducible Python workflows. The objective is not to introduce novel algorithms, but to demonstrate how wavelet-based analysis can be integrated into real analytical pipelines used for monitoring, detection, and interpretation. Readers are encouraged to adapt the provided implementations to their own data sources and operational systems.
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