Week 4: Time-Series Forecasting and Anomaly Detection
Lecture
Status: Lecture notes and slides are available.
Overview
This lecture introduces time-series forecasting, evaluation without future-data leakage, concept drift, and anomaly detection. The two halves connect through a shared question: what behavior should we expect from a changing system?
Materials
| Material | Where |
|---|---|
| Lecture notes | Read notes |
| Slides | Open slides |
References
[1] Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice, 3rd ed. OTexts. Relevant sections: time-series components, autocorrelation, stationarity and differencing, simple forecasting methods, exponential smoothing, ARIMA models, forecast accuracy, temporal evaluation.
[2] Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), Article 44.
[3] Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR.
[4] Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML, PMLR 235, 10148–10167.
[5] Ansari, A. F., et al. (2024). Chronos: Learning the language of time series. Transactions on Machine Learning Research.
[6] Jain, A., & Sen, R. (2026, August 31). TimesFM-3: A zero-shot foundation model for multivariate forecasting. Google Research. Official research report; accessed September 18, 2026.
[7] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), Article 15.
[8] Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. ACM SIGMOD, 93–104.
[9] Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
[10] Pang, G., Shen, C., Cao, L., & van den Hengel, A. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), Article 38.
[11] Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2024). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys, 57(1), Article 15.
[12] Sarfraz, M. S., et al. (2024). Position: Quo Vadis, Unsupervised Time Series Anomaly Detection? ICML, PMLR 235, 43461–43476.
All diagrams are original illustrations. Numerical examples and plotted data are hypothetical.