Time Series and Stochastic Processes ada 21 22 — различия между версиями
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Bdemeshev (обсуждение | вклад) (→Semester II) |
Bdemeshev (обсуждение | вклад) (→Semester II) |
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(не показано 10 промежуточных версии этого же участника) | |||
Строка 9: | Строка 9: | ||
* [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 = | ||
Строка 19: | Строка 20: | ||
= Semester I = | = Semester I = | ||
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+ | <div class="mw-collapsible mw-collapsed" style="width:1000px; overflow: hidden;"> | ||
==== Week 01 ==== | ==== Week 01 ==== | ||
Строка 130: | Строка 133: | ||
Class: Recap on martingales, Ito, etc | Class: Recap on martingales, Ito, etc | ||
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+ | </div> | ||
= Semester II = | = Semester II = | ||
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+ | Do not forget about [https://github.com/bdemeshev/tssp_2021-22/raw/main/ha/tssp_ha.pdf the home assignments!] | ||
==== Week 1 ==== | ==== Week 1 ==== | ||
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+ | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l1_done.pdf Lecture 1]. White noise, stationarity, ACF, PACF | ||
1.1. | 1.1. | ||
− | 1.2. | + | 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 ==== | ==== Week 2 ==== | ||
− | + | [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/TSSP_m3_l2_done.pdf Lecture 2]. | |
− | 2.2. | + | 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 ==== | ==== Week 3 ==== | ||
− | 3.1. | + | [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. | 3.2. Equations is not a process. | ||
Строка 153: | Строка 168: | ||
==== Week 4 ==== | ==== 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]. | 4.1. 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], [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 ==== | ||
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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