Time Series Intelligence (Spring 2026)

Course Information

  • Course Name: Advanced Network Management (ANM)
  • Topic: Time Series Intelligence in Cyberspace
  • Language: English
  • Course Type: Graduate

Course Instructor

Dan Pei
Associate Professor
Department of Computer Science and Technology
Email: peidan(at)tsinghua(dot)edu(dot)cn
Office: Room 1014, Ziqiang Building 1

Course Introduction

Our digital world is a data-driven ecosystem, a dynamic and interconnected network, and this course teaches Temporal Intelligence through Time Series Analysis. It has applications in key domains such as finance, IT operations, the industrial internet, cybersecurity, smart cities, smart manufacturing, smart healthcare, smart energy, and earth sciences. Time-dependent systems are modeled as an Internet of Time Series, consisting of interconnected and interdependent systems. Each state of a system is modeled as a variable whose value changes over time, forming a time series.

The temporal behavior of these systems is analyzed using probabilistic models and advanced time series techniques, where each variable represents a state and the interconnections reflect dependencies among systems.

This course covers various algorithms (both classical and deep learning-based) for time series analysis, including forecasting, anomaly detection, causal discovery and inference, segmentation, time series augmentation, etc from recent research papers in top AI conferences. Then, through case studies of time series tasks, the course further explores time series applications like AIOps (AI for IT Operations).

Finally, the course discusses the latest progress in advanced topics like time series foundation models. Students are expected to complete personal assignments, paper presentations, and a project related to time series algorithms in recent top conferences.

 

Assignments & Project

  1. Individual Assignment: Time Series Visualization and Anomaly Detection
  2. Individual Paper Reading: Select and share a paper from top-tier conferences on time series (Paper list will be provided)
  3. Group Project (1-3 students per group): Self-defined, as long as it is related to time series

Grading Policies

  1. Attendance: 10%
  2. Personal Assignments: 20%
  3. Paper Reading & Presentation: 20%
  4. Group Project: 50%

Syllabus:

Lecture Time Topic
1 Week 1 Introduction
2 Week 2 Time Series Feature Engineering
3 Week 3 Similarity & Distance
4 Week 4 Clustering
5 Week 5 Change Point Detection
6 Week 6 Time Series Classification
7 Week 7-9 Time Series Forecasting
8 Week 10-12 Time Series Anomaly Detection
9 Week 13 Causal Discovery & Inference
10  Week 14-15 Foundation Models
11 Week 16 Project Presentation

 

Other Information

Course Number
80240663

Credit
3

Required text
None.

Reference texts

《Practical time series analysis: Prediction with statistics and machine learning》,  by Aileen Nielsen

《MIT 6.S191 Introduction to Deep Learning 》 with video and slides.

Prerequisites
You are expected to be familiar with Python programming language.

Course Assistant

Zhe Xie
Email: xiez22(at)mails(dot)tsinghua(dot)edu(dot)cn

Previous Courses

 
 
 
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