MITx: Learning Time Series with Interventions course Syllabus

Overview: This course offers a rigorous introduction to time series analysis with a strong emphasis on forecasting and evaluating the impact of interventions. Designed for data professionals seeking advanced skills, the curriculum spans foundational concepts to real-world applications. Learners will develop statistical modeling capabilities essential for measuring causal impacts of policy changes, market events, and other interventions. The course is structured into five core modules and a final project, requiring approximately 120–150 hours of effort over 14–20 weeks. Each module combines theoretical understanding with practical implementation using real-world datasets.

Module 1: Foundations of Time Series Analysis

Estimated time: 30 hours

• Understanding time series data and its components
• Identifying trends, seasonality, and noise
• Stationarity and transformations
• Autocorrelation and partial autocorrelation functions
• Stochastic processes in time-dependent data

Module 2: ARIMA and Forecasting Models

Estimated time: 40 hours

• Autoregressive (AR) models
• Moving average (MA) models
• ARIMA model identification and estimation
• Model diagnostics and residual analysis
• Forecasting with ARIMA on real-world datasets

Module 3: Intervention and Impact Analysis

Estimated time: 40 hours

• Incorporating intervention variables into time series models
• Modeling structural breaks and regime shifts
• Causal impact evaluation using statistical inference
• Measuring effects of policy changes and market events

Module 4: Advanced Applications and Case Studies

Estimated time: 30 hours

• Applying time series models in finance and economics
• Demand forecasting in supply chain analytics
• Healthcare analytics and intervention assessment
• Interpreting model outputs for decision-making

Module 5: Model Assumptions and Limitations

Estimated time: 15 hours

• Understanding assumptions in time series modeling
• Evaluating model robustness
• Recognizing limitations in forecasting and causal inference

Module 6: Final Project

Estimated time: 25 hours

• Apply time series and intervention models to a real-world dataset
• Forecast trends and assess causal impact of an event
• Submit a report interpreting results and model performance

Prerequisites

• Basic knowledge of statistics and probability
• Familiarity with linear algebra concepts
• Experience with statistical software (e.g., R or Python) recommended

What You'll Be Able to Do After

• Analyze time series data for trends, seasonality, and autocorrelation
• Build and validate ARIMA models for forecasting
• Measure the causal impact of interventions using statistical models
• Apply time series techniques in finance, economics, and operations
• Evaluate assumptions and limitations of forecasting models