|
| Approach Type |
Representative Models / Techniques |
Key Features |
Strengths |
Limitations |
Key References |
|
|
| Traditional Statistical Models |
ARIMA, SARIMA, Holt-Winters, Exponential Smoothing |
Assume linearity and stationarity; rely on historical trends |
Simple, interpretable, computationally efficient |
Poor for nonlinear/multivariate data; sensitive to noise and nonstationarity |
Hyndman & Athanasopoulos (2021); Zhang & Kim (2022) |
|
| Statistical Anomaly Detection |
Z-score, Grubbs’ test, Control Charts |
Detects deviations from mean or standard deviation thresholds |
Easy to implement; interpretable |
Fails with non-Gaussian data and dynamic thresholds |
Ahmed et al. (2023) |
|
| Machine Learning Models |
SVM, Random Forest, Gradient Boosting, Prophet, Hybrid ARIMA-ML |
Data-driven, nonlinear modeling |
No need for strict statistical assumptions; flexible |
Heavy feature engineering; limited temporal awareness |
Wang & Zhou (2023); Pérez-Chacón et al. (2022) |
|
| Deep Learning Models (Sequential) |
RNN, LSTM, GRU |
Capture temporal dependencies; learn directly from data |
Effective for sequence learning; strong predictive accuracy |
Vanishing gradient; limited scalability |
Lim & Zohren (2021) |
|
| Deep Learning Models (Convolutional) |
Temporal Convolutional Networks (TCN) |
Uses dilated convolutions for long-term patterns |
Parallelizable; efficient |
May overlook global temporal context |
Bai et al. (2023) |
|
| Transformer-Based Models |
Temporal Fusion Transformer (TFT), Informer, TimesNet |
Self-attention for long-range dependencies; interpretable embeddings |
High scalability; superior multivariate handling |
Requires large datasets and tuning |
Xu et al. (2024); Lai et al. (2023) |
|
| AI-Based Anomaly Detection |
Autoencoder, VAE, GAN, GNN, Attention-based models |
Learn representations of normal behavior to flag deviations |
Works in unsupervised settings; handles multivariate data |
Limited interpretability; high computation |
Darban et al. (2022); Iqbal et al. (2024); Chiranjeevi et al. (2024) |
|
| Emerging Hybrid / Edge Models |
Physics-informed NN, Federated Learning, XAI frameworks |
Combines interpretability, causality, and scalability |
Explainable; data-efficient; privacy-preserving |
Still developing; less standardized |
Lee & Park (2024); Chen et al. (2024); Méndez et al. (2024) |
|
