This work is like testing two different "crystal balls" for car data: one based on classic math waves (Fourier series) and one based on modern neural networks (deep learning) to see which predicts complex automotive signals better and when.
Provides evidence-based guidance on when to use deep learning versus traditional Fourier-based models for modeling and predicting complex automotive signals (e.g., vibration, acoustics, or other periodic/oscillatory measurements), helping teams avoid over-engineering with deep learning when simpler models are sufficient and cheaper.
Methodological insight and comparative benchmarks that can be embedded into internal modeling standards and toolkits for automotive signal analysis and control, creating a process and know‑how moat rather than a pure data moat.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Training cost and data volume requirements for deep learning models versus simpler Fourier models; potential overfitting and generalization issues on limited or noisy automotive datasets.
Early Majority
Direct, peer‑reviewed comparison of deep learning architectures with classical Fourier series models for automotive-style signals, giving practitioners concrete guidance on tradeoffs rather than only showcasing deep learning performance in isolation.