Week 6: Practical Applications of Time-Series and Anomaly Detection
Practical Session
Overview
Present a paper on time-series forecasting or anomaly detection in 10–15 minutes. Explain the research in your own words for classmates who have not read the paper. Choose one paper from the list below.
Your presentation should cover:
- Problem and motivation: What did the authors investigate, and why was it needed?
- Data and method: What data did they use, how does the method work, and what is new?
- Experiments and results: How was the method evaluated, and what do the main results show?
- Critical assessment: What are the strengths and limitations? What could be improved or tested next?
- Conclusion and critique: What are the main takeaways from the paper, and what is your own critical view of the work?
Use clear, readable slides, explain unfamiliar terms, cite your sources, and stay within the allotted time. Be ready to discuss your interpretation and answer questions.
Materials
| Material | Where |
|---|---|
| Lecture notes | Coming soon |
| Slides | Coming soon |
| Lab | - |
| Papers | Paper list on this page |
Papers
Time-series forecasting
| No. | Paper |
|---|---|
| 1 | Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift Kim et al. (2022), ICLR. |
| 2 | A Decoder-Only Foundation Model for Time-Series Forecasting Das et al. (2024), ICML. |
| 3 | LSTM Based Long-Term Energy Consumption Prediction with Periodicity Wang et al. (2020), Energy. |
| 4 | Residential Energy Consumption Forecasting Using Deep Learning Models Ramos et al. (2023), Applied Energy. |
Anomaly detection
| No. | Paper |
|---|---|
| 5 | Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy Xu et al. (2022), ICLR. |
| 6 | TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data Tuli et al. (2022), Proceedings of the VLDB Endowment. |
| 7 | Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data Kim et al. (2021), Sensors. |
| 8 | Real-Time Anomaly Detection on Time Series of Industrial Furnaces: A Comparison of Autoencoder Architectures Pota et al. (2023), Engineering Applications of Artificial Intelligence. |