MATH-342: Time series

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Lectures in this course (41)

Time Series: Multi-Tapering and Parametric Estimation

Covers Multi-Tapering and Parametric Estimation in Time Series analysis, including spectral estimation and AR model fitting.

Integrated and Seasonal Processes: Time Series

Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.

Spectral Estimation in Time Series

Covers spectral estimation in time series analysis, including imaging kernels, tapering methods, and AR models.

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Time Series: Spectral Estimation & Yule Walker

On Time Series explores Spectral Estimation, Yule Walker method, and ARIMA models.

Time Series Analysis: ARIMA and Seasonal Models

Covers ARIMA, ARI, and seasonal models for time series analysis.

Time Series: Parametric Estimation

Covers parametric estimation, seasonal modeling, Box-Jenkins methods, variance calculations, and dependence measures in time series analysis.

Residuals & Forecasting: MATH-342 Time Series

Covers residuals, diagnostics, overfitting, and forecasting methods in time series analysis.

Model Specification in Time Series

Covers the identification and model specification in time series analysis, including AR models and least squares estimation.

Estimation and Forecasting in Time Series

Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.

Forecasting & Long Memory: Time Series

Explores forecasting methods and long memory in time series analysis.

Time Series

Explores Time Series, covering model specification, diagnostics, and forecasting methods.

Model Choice and Prediction

Explores model choice, prediction, and forecasting techniques in time series analysis.

Long Memory and ARCH: Time Series

Explores long memory in time series and ARCH models for financial volatility.

Time Series: Forecasting and Long Memory

Explores forecasting in time series analysis, long memory processes, and ARCH models for volatility modeling.

Structural Modelling and the Kalman Filter: Time Series

Explores structural modelling in time series and introduces the Kalman filter for prediction and estimation.

Long Memory and ARCH: Time Series Math 342

Explores long memory in time series and Autoregressive Conditional Heteroskedasticity processes in financial data.

Vector Autoregression

Explores Vector Autoregression for modeling vector-valued time series, covering stability, Yule-Walker equations, and spectral representation.

Vector Autoregression: Modeling Vector-Valued Time Series

Explores Vector Autoregression for modeling vector-valued time series, covering stability, reverse characteristic polynomials, Yule-Walker equations, and autocorrelations.

Kalman Filter: Time Series

Covers structural modeling, state space models, and the Kalman filter in time series analysis.

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