AI for Time Series and Anomaly Detection

Journal of Artificial Intelligence and Big Data | Vol 4, Issue 2

Table 1. Comparison of Traditional, Machine Learning, and DeepLearning Approaches for Time Series Forecasting and Anomaly Detection

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)