Time Series Intelligence (Spring 2026)

Course Information

  • Course Name: Advanced Network Management (ANM)
  • Topic: Time Series Intelligence
  • 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

This course teaches time series intelligence for the digital world. Time series intelligence has applications in key domains such as finance, smart cities, smart manufacturing, smart healthcare, smart energy, IT operations,  cybersecurity, etc. This course covers various time series specific algorithms. including Feature Engineering, Pairwise Distance & Similarity Clustering, Change Point Detection, Classification, Forecasting, Anomaly Detection, Spatiotemporal Model,  Causal Discovery & Inference,  Multimodal Foundation Models.

 

Assignments & Project

  1. Individual Assignment: Time Series Visualization and Anomaly Detection
  2. Individual Paper Reading: Select and share a paper from top-tier AI 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.
    • Recent projects include Stock Price Prediction, Sleep Stage Detection, Road Traffic Anomaly Detection, Olfactory Sensor Analysis .

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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