Practical Time Series Analysis Course Syllabus

Overview: This course provides a comprehensive introduction to time series analysis, combining theoretical foundations with hands-on practice using R. Learners will explore core concepts such as stationarity, autocorrelation, and seasonality, and apply statistical models including AR, MA, ARIMA, and SARIMA to real-world datasets. The course spans approximately 25 hours, offering flexible pacing ideal for working professionals. Each module builds practical skills in modeling and forecasting temporal data, culminating in a final project that integrates all learned techniques.

Module 1: Basic Statistics

Estimated time: 3 hours

• Review of fundamental statistical concepts
• Introduction to R programming for data analysis
• Data types and structures in R
• Basic data manipulation and summary statistics

Module 2: Visualizing Time Series and Beginning to Model Time Series

Estimated time: 3 hours

• Plotting time series data in R
• Identifying trends and patterns
• Decomposition of time series components
• Introduction to modeling frameworks

Module 3: Stationarity, MA(q), and AR(p) Processes

Estimated time: 5 hours

• Understanding stationarity and its importance
• Moving Average (MA) models of order q
• Autoregressive (AR) models of order p
• Simulating and identifying MA and AR processes

Module 4: AR(p) Processes, Yule-Walker Equations, PACF

Estimated time: 5 hours

• Yule-Walker equations for parameter estimation
• Partial Autocorrelation Function (PACF)
• Model identification using ACF and PACF
• Fitting AR models in R

Module 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models

Estimated time: 5 hours

• Model selection using Akaike Information Criterion (AIC)
• Autoregressive Moving Average (ARMA) models
• Autoregressive Integrated Moving Average (ARIMA) models
• Parameter estimation and diagnostic checking

Module 6: Seasonality, SARIMA, Forecasting

Estimated time: 4 hours

• Identifying seasonal patterns in time series
• Seasonal ARIMA (SARIMA) modeling
• Forecasting with seasonal adjustments
• Evaluating forecast accuracy

Module 7: Final Project

Estimated time: 5 hours

• Apply time series models to a real-world dataset
• Perform model selection and diagnostics
• Generate and interpret forecasts

Prerequisites

• Proficiency in R programming
• Familiarity with basic statistics (mean, variance, correlation)
• Basic understanding of probability distributions

What You'll Be Able to Do After

• Understand and interpret time series data patterns
• Apply AR, MA, ARIMA, and SARIMA models effectively
• Use R for time series visualization and modeling
• Perform accurate forecasting with diagnostic validation
• Enhance data analysis capabilities in finance, economics, or supply chain roles