Time Series Analysis and Forecasting

1. General Information

2. Department and Instructor

Department: Faculty of Data Science and Artificial Intelligence, National Economics University.

Address: Suite 1105, Building A1, NEU.

Dr. Trong-Nghia Nguyen

Email: nghiant@neu.edu.vn

Website: http://nghianguyen7171.github.io/

Office Hours: To be determined

MSc. Thai-Hoa Ngo

Email: hoatn@neu.edu.vn

Website: https://fda.neu.edu.vn/fda-members/ths-ngo-thai-hoa/

3. Course Description

This course provides a practical introduction to time series analysis and forecasting with applications in finance, economics, and healthcare. Students use Python (NumPy, Pandas, Matplotlib, Statsmodels, Scikit-learn, and optionally deep learning frameworks) to work with real-world time-indexed data.

Core topics include time series concepts and components, exploratory analysis, stationarity, ARIMA and SARIMA models. In later weeks, students are introduced to machine learning and deep learning methods for time series, such as tree-based models and simple recurrent or convolutional neural networks.

The course is strongly project-based with groups working on real datasets (e.g., stock prices, macroeconomic indicators, vital signs, EEG/ECG signals). Labs focus on project progress. A Midterm Project Baseline replaces a written exam; the final deliverable is a comprehensive group project combining classical and ML/DL methods.

4. Learning Resources

Main textbooks

• Gebhard Kirchgässner, Jürgen Wolters (2013), Introduction to Modern Time Series Analysis, Springer.
• Wes McKinney (2022), Python for Data Analysis, O’Reilly Media.
• Eryk Lewinson (2022), Python for Finance Cookbook, Packt Publishing.

Other references

• Tarek A. Atwan (2022), Time Series Analysis with Python Cookbook, Packt Publishing.
• Changquan Huang & Alla Petukhina (2022), Applied Time Series Analysis and Forecasting with Python, Springer.
• Ben Auffarth (2021), Machine Learning for Time-Series with Python, Packt Publishing.
• Francesca Lazzeri (2020), Machine Learning for Time Series Forecasting with Python, Wiley.
• Online notes: https://online.stat.psu.edu/stat510/

Student Desk

5. Course Objectives

• G1. Understand fundamental time series concepts and applications in finance, economics, healthcare.
• G2. Collect, preprocess, and manipulate time series data in Python using appropriate libraries.
• G3. Conduct exploratory time series analysis, including visualization, decomposition, and stationarity assessment.
• G4. Model and forecast time series data using core statistical methods such as ARIMA and SARIMA.
• G5. Apply and compare traditional time series models with basic machine learning and deep learning methods.
• G6. Develop teamwork, self-study, and shared responsibility through project-based learning.

6. Course Learning Outcomes

• CLO 1.1 (G1): Explain key time series concepts and real-world relevance.
• CLO 1.2 (G2): Use Python (Pandas, NumPy, Matplotlib, Statsmodels) to load, preprocess, and visualize time series.
• CLO 2.1 (G3): Perform exploratory analysis using ACF/PACF, decomposition, and smoothing.
• CLO 3.1 (G3, G4): Explain stationarity and apply differencing and ADF tests.
• CLO 3.2 (G4): Build and interpret AR, MA, and ARMA models in Python.
• CLO 4.1 (G4): Build, evaluate, and diagnose ARIMA and SARIMA models using Box–Jenkins.
• CLO 4.2 (G5): Transform time series into supervised format and implement ML models (e.g., tree-based).
• CLO 4.3 (G5): Describe and implement simple DL models (RNN/LSTM/CNN) for time series.
• CLO 5.1 (G5): Design and implement a complete forecasting pipeline on a real dataset, combining classical and ML/DL approaches.
• CLO 5.2 (G5): Analyze and interpret results, compare methods, and provide insights.
• CLO 6.1 (G6): Demonstrate self-study and independent work via homework and project contributions.
• CLO 6.2 (G6): Collaborate effectively in teams and report progress clearly.

7. Course Assessment

Lab weeks are used for group progress presentations; midterm baseline replaces a written exam.

8. Teaching Plan (15 Weeks)