Module #1 Introduction to Time Series Analysis Overview of time series data, importance of time series analysis, and common applications
Module #2 Types of Time Series Data Exploration of different types of time series data, including univariate, multivariate, and panel data
Module #3 Time Series Components Decomposition of time series data into trend, seasonality, and residuals
Module #4 Time Series Visualization Visualizing time series data using plots, charts, and other graphical methods
Module #5 Stationarity and Non-Stationarity Understanding stationarity and non-stationarity in time series data
Module #6 Time Series Transformations Transforming time series data to achieve stationarity, including differencing and log transformations
Module #7 Autocorrelation and Partial Autocorrelation Analyzing autocorrelation and partial autocorrelation functions to understand time series patterns
Module #8 Introduction to ARIMA Models Overview of Autoregressive Integrated Moving Average (ARIMA) models and their components
Module #9 ARIMA Model Identification Identifying the optimal ARIMA model using information criteria and model diagnostics
Module #10 ARIMA Model Estimation and Forecasting Estimating and forecasting with ARIMA models, including confidence intervals and prediction intervals
Module #11 Seasonal Decomposition and Seasonal ARIMA Decomposing time series data into seasonal and trend components, and applying seasonal ARIMA models
Module #12 Exponential Smoothing (ES) Methods Introduction to exponential smoothing methods, including simple, trend, and seasonal ES
Module #13 Arch and GARCH Models Modeling volatility using Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized ARCH (GARCH) models
Module #14 Vector Autoregression (VAR) Models Modeling multivariate time series data using vector autoregression models
Module #15 Machine Learning for Time Series Forecasting Applying machine learning algorithms to time series forecasting, including regression, decision trees, and neural networks
Module #16 Ensemble Methods for Time Series Forecasting Combining multiple models for improved time series forecasting, including bagging, boosting, and stacking
Module #17 Evaluating Time Series Forecasting Models Evaluating the performance of time series forecasting models using metrics such as MAE, MSE, and R-squared
Module #18 Cross-Validation for Time Series Forecasting Applying cross-validation techniques to time series forecasting models
Module #19 Handling Missing Values and Outliers Methods for handling missing values and outliers in time series data, including imputation and robust estimation
Module #20 Time Series Analysis in Python Hands-on practice with popular Python libraries for time series analysis, including Pandas, Statsmodels, and Prophet
Module #21 Time Series Analysis in R Hands-on practice with popular R libraries for time series analysis, including Forecast and Zoo
Module #22 Case Studies in Time Series Analysis Real-world examples and case studies in time series analysis, including finance, economics, and healthcare
Module #23 Common Challenges in Time Series Analysis Addressing common challenges and pitfalls in time series analysis, including overfitting and model selection
Module #24 Best Practices for Time Series Analysis Best practices for time series analysis, including model validation, model interpretation, and communication of results
Module #25 Course Wrap-Up & Conclusion Planning next steps in Time Series Analysis and Forecasting career
