Module 1: Watch the video to learn more about Introduction, Fundamental Concepts, Nature of Time Series Data with Examples, Stochastic Process, White Noise, Means, Variances, and Covariances, Random Walk, Stationarity in Time Series Analysis.

Module 2: Watch the video to learn more about Deterministic and Stochastic Trends, Regression Methods, Cyclical or Seasonal Trends, Cosine Trends, Interpreting Regression Output, Reliability and Efficiency of Regression Estimates in Time Series Analysis.

Module 3: Watch the video to learn more about Models for Stationary Time Series I, General Linear Processes, Moving Average Processes, MA(p), Autoregressive Processes, AR(q), Yule-Walker equations in Time Series Analysis.

Module 4: Watch the video to learn more about Models for Stationary Time Series II, Mixed Autoregressive Moving Average Model, ARMA(p,q), Invertibility, Stationarity Region for an AR(2) Process, Autocorrelation Function for ARMA(p,q) in Time Series Analysis.

Module 5: Watch the video to learn more about Models for Nonstationary Time Series, Stationarity Through Differencing, ARIMA Models, Constant Terms in ARIMA Models, Transformations, Backshift Operator in Time Series Analysis.

Module 6: Watch the video to learn more about Model Specification, Sample, Partial, and Extended Autocorrelation Function, Nonstationarity, Overdifferencing, The Dickey-Fuller Unit-Root Test in Time Series Analysis.

Module 7: Watch the video to learn more about Parameter Estimation, Method of Moments, (Unconditional) Least Squares Estimation, Maximum Likelihood Estimators, Asymptotic Properties of the Estimates, Bootstrapping ARIMA Models in Time Series Analysis.

Module 8: Watch the video to learn more about Model Diagnostics, Residual Analysis, Normality and Autocorrelation of the Residuals, Ljung-Box Test, Identifiability, Overfitting and Parameter Redundancy in Time Series Analysis.

Module 9: Watch the video to learn more about Forecasting, ARIMA Forecasting, Prediction Limits, Updating ARIMA Forecasts, Forecast Weighted Moving Averages, Forecasting Transformed Series, State Space Models, Kalman filtering in Time Series Analysis.

Module 10: Watch the video to learn more about Seasonal Models, Seasonal ARIMA Models, Multiplicative Seasonal ARMA Models, Nonstationary Seasonal ARIMA Models, Seasonal Model Specification, Fitting, and Checking, Forecasting Seasonal Models in Time Series Analysis.

Module 11: Watch the video to learn more about Time Series Models of Heteroscedasticity, Some Common Features of Financial Time Series, ARCH Models, GARCH Models, Long Memory ARMA and Fractional Differencing in Time Series Analysis.

Module 12: Watch the video to learn more about Group Course Project of Time Series Analysis.

Module 13: Watch the video to learn more about Multivariate Time Series Analysis, VAR Models, VMA Models, VARMA Models in Time Series Analysis.

Research

Broadly, my research consists of developing statistical tools for analysing complex dynamic and dependent data, in particular in the context of dimension reduction for multivariate time series. Technically, I conduct theoretical and applied research in Bayesian statistics, multivariate time series, factor analysis, cluster analysis, copula models, and dependencies. My current research has benefited greatly from collaborations with researchers inside and outside of statistics. Please find my research profile on Google Scholar.