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Time Series Analysis

Classes will be held online via ZOOM at the scheduled time (Tuesday 14:00-15:30). Please see the Content section for details and a web link. A time series is a sequence (x_t) of data where the subscript t indicates the time at which the datum x_t was observed. The course provides an introduction to the theory and practice of statistical time series analysis. Topics covered include stationary and non-stationary stochastic processes, autoregressive and moving average (ARMA) models, model selection and estimation, state-space models and the Kalman filter, forecasting and forecast evaluation, and an outline of spectral techniques. Knowledge of the contents of modules M-MATH-101321 (Introduction to Stochastics) and M-MATH-101322 (Probability Theory) is essential. Furthermore, familiarity with the contents of module M-MATH-103220 (Statistics) is strongly recommended.

General Information

Important Information
Classes will be held online via Zoom at the scheduled slot (Tuesday 14:00-15:30). Technical details will be announced as they become available. Please check back Tuesday morning, April 21, and make sure to participate in the first class session. Students will be asked to introduce themselves briefly then.
Syllabus
A time series is a sequence (x_t) of data where the subscript t indicates the time at which the datum x_t was observed. The course provides an introduction to the theory and practice of statistical time series analysis. Topics covered include stationary and non-stationary stochastic processes, autoregressive and moving average (ARMA) models, model selection and estimation, state-space models and the Kalman filter, forecasting and forecast evaluation, and an outline of spectral techniques.

Knowledge of the contents of modules M-MATH-101321 (Introduction to Stochastics) and M-MATH-101322 (Probability Theory) is essential. Furthermore, familiarity with the contents of module M-MATH-103220 (Statistics) is strongly recommended.

Description

Classes will be held online via ZOOM at the scheduled time (Tuesday 14:00-15:30). Please see the Content section for details and a web link.

A time series is a sequence (x_t) of data where the subscript t indicates the time at which the datum x_t was observed. The course provides an introduction to the theory and practice of statistical time series analysis. Topics covered include stationary and non-stationary stochastic processes, autoregressive and moving average
(ARMA) models, model selection and estimation, state-space models and the Kalman filter, forecasting and forecast evaluation, and an outline of spectral techniques.

Knowledge of the contents of modules M-MATH-101321 (Introduction to Stochastics) and M-MATH-101322 (Probability Theory) is essential. Furthermore, familiarity with the contents of module M-MATH-103220
(Statistics) is strongly recommended.

General

Language
English
Author
Johannes Resin, Tilmann Gneiting
Copyright
This work has all rights reserved by the owner.

Availability

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Unlimited
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Unlimited

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1551600
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Created on
08. Apr 2020, 13:14