Time Series and Stochastic Processes ada 21 22 — различия между версиями
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Bdemeshev (обсуждение | вклад) |
Bdemeshev (обсуждение | вклад) (→Semester II) |
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(не показаны 22 промежуточные версии этого же участника) | |||
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* [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf All home Assignments] | * [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf All home Assignments] | ||
+ | * Notes for [https://github.com/bdemeshev/tssp_2021-22/tree/main/lectures lectures] and [https://github.com/bdemeshev/tssp_2021-22/tree/main/notes classes]. | ||
= Teachers and assistants = | = Teachers and assistants = | ||
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− | = | + | = Semester I = |
+ | |||
+ | <div class="mw-collapsible mw-collapsed" style="width:1000px; overflow: hidden;"> | ||
==== Week 01 ==== | ==== Week 01 ==== | ||
− | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_01_TSSP.pdf] |
Class: First step analysis, expected time to get HTH. | Class: First step analysis, expected time to get HTH. | ||
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==== Week 02 ==== | ==== Week 02 ==== | ||
− | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_02_TSSP.pdf] |
Class: Markov chain states classification | Class: Markov chain states classification | ||
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==== Week 03 ==== | ==== Week 03 ==== | ||
− | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_03_TSSP.pdf] |
Class: Poisson process. | Class: Poisson process. | ||
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==== Week 04 ==== | ==== Week 04 ==== | ||
− | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_04_TSSP.pdf] |
− | Class: | + | Class: Conditional expected value. Conditional variance. |
==== Week 05 ==== | ==== Week 05 ==== | ||
− | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_05_TSSP.pdf] |
− | Class: Conditional expected value | + | Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra. |
==== Week 06 ==== | ==== Week 06 ==== | ||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
Lecture: | Lecture: | ||
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Duration: 120 minutes. No proctoring. | 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 | ||
+ | |||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-10-28 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: Martingales in discrete time | ||
+ | |||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-11-09 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: Wiener process definition, basic properties, inversion | ||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-11-16 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: Stochastic integral, intuition, limit in L2 | ||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-11-23 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: Stochastic integral properties, Ito's lemma | ||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-11-30 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: BS model, Girsanov theorem, pricing | ||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-12-07 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: more pricing examples in BS model | ||
+ | |||
+ | ==== Week ==== | ||
+ | |||
+ | Date: 2021-12-14 | ||
+ | |||
+ | Lecture: | ||
+ | |||
+ | Class: Recap on martingales, Ito, etc | ||
+ | |||
+ | </div> | ||
+ | |||
+ | = Semester II = | ||
+ | |||
+ | Do not forget about [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf the home assignments!] | ||
+ | |||
+ | ==== Week 1 ==== | ||
+ | |||
+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l1_done.pdf Lecture 1]. White noise, stationarity, ACF, PACF | ||
+ | |||
+ | 1.1. | ||
+ | |||
+ | 1.2. Predictive interval for random walk, difference between mean, mode and median: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-14-sem2_class_01_2_b.pdf pdf-b] | ||
+ | |||
+ | ==== Week 2 ==== | ||
+ | |||
+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l2_done.pdf Lecture 2]. | ||
+ | |||
+ | 2.1. ETS model, forecasting, decomposition: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-18-sem2_class_02_1_a.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-18-sem2_class_02_1_b.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-18-sem2_class_02_1_c.pdf pdf-c] | ||
+ | |||
+ | 2.2. AR(2), expected value, covariances: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_a.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_b.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-20-sem2_class_02_2_c.pdf pdf-c] | ||
+ | |||
+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/arma_no_nonsense/arma_no_nonsense.pdf Arma notes without nonsense] | ||
+ | |||
+ | ==== Week 3 ==== | ||
+ | |||
+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l3_done.pdf Lecture 3]. | ||
+ | |||
+ | 3.1. Non stationarity of ETS(AAA), solutions of recurrence equation: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-26-sem2_class_03_1_b.pdf, pdf-b] | ||
+ | |||
+ | 3.2. Equations is not a process. | ||
+ | Two problems from [https://new.universiade-ecm.com/ Econometrics Olympiad]: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-27-sem2_class_03_2_a_rus.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-27-sem2_class_03_2_b_eng.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-01-28-sem2_class_03_2_c_rus.pdf pdf-c]. | ||
+ | |||
+ | ==== Week 4 ==== | ||
+ | |||
+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l4_done.pdf Lecture 4]. | ||
+ | |||
+ | 4.1. Solutions of recurrence equation: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-01-sem2_class_04_1_a_rus_arma_sols.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-01-sem2_class_04_1_b_rus_arma_sols.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-01-sem2_class_04_1_c_eng_arma_sols.pdf pdf-c]. | ||
+ | |||
+ | 4.2. Roots of lag and characteristic equation: [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-03-sem2_class_04_2_a_eng_roots.pdf pdf-a], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-03-sem2_class_04_2_b_rus_roots.pdf pdf-b], [https://github.com/bdemeshev/tssp_2021-22/raw/main/notes/tssp_2021-22_2022-02-03-sem2_class_04_2_c_rus_roots.pdf pdf-c]. | ||
+ | |||
+ | ==== Week 5 ==== | ||
+ | |||
+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l5_done.pdf Lecture 5] | ||
+ | |||
+ | Estimation of ETS and ARMA: [https://colab.research.google.com/drive/1LE9T0KnnUBM-1OIzWT3INJzJjKd5-GdX?usp=sharing colab notebook] | ||
+ | |||
+ | ==== Week 6 ==== | ||
+ | |||
+ | |||
+ | |||
== Sources == | == Sources == | ||
+ | |||
+ | * [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-2021] | ||
+ | |||
+ | * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-2021] | ||
+ | * [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-2022] | ||
* [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook] | * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook] | ||
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* [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains | * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains | ||
− | * [https://eml.berkeley.edu/reprints/misc/understanding.pdf | + | * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm] |
− | + | ||
− | + | ||
− | * [http:// | + | * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler] |
− | * [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains | + | * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains] |
* [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods] | * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods] | ||
− | * [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf | + | * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!) |
− | + | ||
− | + | ||
=== Stochastic Calculus === | === Stochastic Calculus === | ||
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* [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page] | * [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay web page] | ||
− | * [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html | + | * Van der Vaart, [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Time Series] |
* [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)] | * [https://github.com/bdemeshev/ts_pset Черновик задачника (рус)] | ||
Строка 122: | Строка 239: | ||
==== UCM ==== | ==== UCM ==== | ||
− | * [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html | + | * 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 | + | * 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] | ||
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* [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models] | * [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models] | ||
− | |||
== Grading System == | == Grading System == |
Текущая версия на 18:08, 12 февраля 2022
General course info
- Boring official web page
- teams group: all class videos are there :)
Teachers and assistants
Lecturer: Peter Lukianchenko
Class teacher: Boris Demeshev
Semester I
Semester II
Do not forget about the home assignments!
Week 1
Lecture 1. White noise, stationarity, ACF, PACF
1.1.
1.2. Predictive interval for random walk, difference between mean, mode and median: pdf-b
Week 2
2.1. ETS model, forecasting, decomposition: pdf-a, pdf-b, pdf-c
2.2. AR(2), expected value, covariances: pdf-a, pdf-b, pdf-c
Week 3
3.1. Non stationarity of ETS(AAA), solutions of recurrence equation: pdf-b
3.2. Equations is not a process. Two problems from Econometrics Olympiad: pdf-a, pdf-b, pdf-c.
Week 4
4.1. Solutions of recurrence equation: pdf-a, pdf-b, pdf-c.
4.2. Roots of lag and characteristic equation: pdf-a, pdf-b, pdf-c.
Week 5
Estimation of ETS and ARMA: colab notebook
Week 6
Sources
MC + MCMC
- James Norris, Markov chains (1998, no kernels)
- Cambridge course on Markov chains
- Chib and Greenberg, Understanding MH algorithm
- Casella, Explaining Gibbs Sampler
- Roberts and Rosenthal, General State Space Markov Chains
- Charles Geyer, MCMC lecture notes (with a little bit of kernels!)
Stochastic Calculus
- Zastawniak, Basic Stochastic Processes
Time Series
- Van der Vaart, Time Series
UCM
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter