2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Mechanical Engineering
Time Sequencial Data Analysis
- Academic unit or major
- Undergraduate major in Mechanical Engineering
- Instructor(s)
- Yoshifumi Nishida
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-4 Fri (I1-256(I121))
- Class
- -
- Course Code
- MEC.B334
- Number of credits
- 110
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Jul 22, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Data that changes over time and is arranged in chronological order is referred to as time series data. Our daily lives are filled with time series data (weather data, measurement data using sensors, stock price data, etc.) In this lecture, we will learn the basics of handling time series data.
Course description and aims
● We will learn the basics of digital signal processing, such as spectral analysis to understand the characteristics of data and digital filters to extract necessary signals.
● We will learn the basics of time series modeling to perform smoothing and prediction using various time series data.
Keywords
Digital signal processing, digital filters, Fourier transform, time series data analysis, time series modeling, autoregressive model, state space model
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
We will deepen your understanding by combining lectures on basic theory with practical training using MATLAB.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to Time Series Data Analysis | Introduction to Time Series Data Analysis |
| Class 2 | Introduction to Time Series Data Analysis (application examples) | Introduction to Time Series Data Analysis (application examples) |
| Class 3 | Using Matlab for time series data analysis | Using Matlab for time series data analysis |
| Class 4 | Using Matlab for time series data analysis (demonstration with sample codes) | Using Matlab for time series data analysis (demonstration with sample codes) |
| Class 5 | Digital Signal Processing Fundamentals | Digital Signal Processing Fundamentals |
| Class 6 | Digital Signal Processing Fundamentals (demonstration with sample codes) | Digital Signal Processing Fundamentals (demonstration with sample codes) |
| Class 7 | Fundamentals of real-world data processing and modeling | Fundamentals of real-world data processing and modeling |
| Class 8 | Fundamentals of real-world data processing and modeling (demonstration with sample codes) | Fundamentals of real-world data processing and modeling (demonstration with sample codes) |
| Class 9 | Time Series Modeling Using Autoregressive Models | Time Series Modeling Using Autoregressive Models |
| Class 10 | Time Series Modeling Using Autoregressive Models (demonstration with sample codes) | Time Series Modeling Using Autoregressive Models (demonstration with sample codes) |
| Class 11 | Time Series Modeling Using State Space Models | Time Series Modeling Using State Space Models |
| Class 12 | Time Series Modeling Using State Space Models (demonstration with sample codes) | Time Series Modeling Using State Space Models (demonstration with sample codes) |
| Class 13 | Advanced Topics in Time Series Data Analysis | Advanced Topics in Time Series Data Analysis |
| Class 14 | Final exam | Final exam |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to review class content afterwards (including assignments) for each class.
They should do so by referring to the course material.
Textbook(s)
Necessary materials will be distributed during the lectures.
Reference books, course materials, etc.
Necessary materials will be distributed during the lectures. Other reference books will be introduced during the lecture.
Evaluation methods and criteria
Short report at every class: 50%
Final exam: 50%
If the final exam cannot be held, it may be replaced by a report or other examination.
Related courses
- MEC.B201 ： Fundamentals of information and mathematical sciences
- MEC.B221 ： Statistical data analysis
Prerequisites
None
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
nishida.y.af[at]m.titech.ac.jp