Time Series and Stochastic Processes ada 21 22 — различия между версиями

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(Week progress)
(UCM)
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==== UCM ====
 
==== UCM ====
  
* [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html aa]
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* Harvey Jaeger, [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Detrending, Stylized Facts and the Business Cycle]
  
* [https://core.ac.uk/download/pdf/6242335.pdf bb]
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* João Tovar Jalles, [https://core.ac.uk/download/pdf/6242335.pdf Structural Time Series Models and the Kalman Filter]
  
 
* [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]
 
* [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey, Forecasting with UCM]

Версия 19:59, 27 октября 2021

General course info


Teachers and assistants

Lecturer: Peter Lukianchenko

Class teacher: Boris Demeshev


Week progress

Week 01

Lecture:

Class: First step analysis, expected time to get HTH.

Week 02

Lecture:

Class: Markov chain states classification


Week 03

Lecture:

Class: Poisson process.

Week 04

Lecture:


Class: Conditional expected value. Conditional variance.

Week 05

Lecture:

Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra.

Week 06

Lecture:

Class: Probability limit, Moment generating function


Midterm

The long-awaited midterm will be on 28 October, 10:00 - 12:00.

Duration: 120 minutes. No proctoring.

Topics:

  • First step analysis
  • Classification of states and classes of MC.
  • Conditional expected value (two views).
  • Poisson process.
  • Sigma algebras.
  • Probability limit
  • Moment generating function

Sources

MC + MCMC

  • James Norris, Markov chains (1998, no kernels)


Stochastic Calculus

  • Zastawniak, Basic Stochastic Processes

Time Series

UCM

Grading System