Time Series: Representation and Modelling

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Related lectures (32)

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Time Series: Structural Modelling and Kalman Filter

Covers structural modelling, Kalman Filter, stationarity, estimation methods, forecasting, and ARCH models in time series.

Parametric Signal Models: Matlab Practice

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Time Series: Stochastic Properties and Modelling

Explores the stochastic properties and modelling of time series, covering autocovariance, stationarity, spectral density, estimation, forecasting, ARCH models, and multivariate modelling.

Time Series: Fundamentals and Models

Covers the fundamentals of time series analysis, including models, stationarity, and practical aspects.

Integrated and Seasonal Processes: Time Series

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

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Multivariate Time Series: Cointegration & Forecasting

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Univariate time series: Analysis & Modeling

Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.

Time Series Models: Autoregressive Processes

Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.

Forecasting & Long Memory: Time Series

Explores forecasting methods and long memory in time series analysis.