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Stochastic processes and applications DSBA 2026/2027

2026-09-09T06:21:18Z

Ianovosad: /* Course links */ добавил ссылку на ведомость

<div style="padding:0; margin:0 0 24px;">
<span style="font-size:90%; letter-spacing:2px;">FCS · HSE · 2026/27</span><br />
<span style="font-size:190%; line-height:1.25;">'''Stochastic Processes and Applications'''<br />'''Time Series Analysis'''</span><br />
<span>Data Science and Business Analytics</span>
</div>

<div style="border:1px solid #aaa; padding:12px 16px; margin:0 0 20px;">
<span lang="ja">侍には目標がなく道しかない</span><br />
''Samurai niwa mokuhyō ga naku michi shikanai.''<br />
'''A samurai has no goal, only a path.'''
</div>

''Random walks. Occasional enlightenment.''

<div style="padding:10px 14px; margin:16px 0;">
[https://t.me/+oM4HmZaMsV1lOWIy '''Course chat'''] · [[#Teaching team and contacts|Teaching team]] · [[#Rules of the game|Rules of the game]] · [[#Sources of Wisdom|Sources of Wisdom]]
</div>

''The chat may not work in the parking lot. The Markov property still does.''

== Course information ==

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Course !! Year of study !! Modules !! Credits !! Language
|-
| Stochastic Processes and Applications (SP) || 3 || 1–2 || 4 || English
|-
| Time Series Analysis (TSA) || 3 || 3–4 || 4 || English
|}

SP covers stochastic models, including Markov chains, random walks and martingales. TSA focuses on modelling and forecasting time series. Prerequisites include probability, statistical inference and linear algebra.

Official course information: [https://www.hse.ru/ba/data/courses/1163520553.html SP 2026/27] · [https://www.hse.ru/ba/data/courses/1163520577.html TSA 2026/27].

== Course links ==

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Resource !! Link
|-
| Telegram course chat || [https://t.me/+oM4HmZaMsV1lOWIy SP–TSA course chat]
|-
| Gradebook || [https://docs.google.com/spreadsheets/d/1PdbMTFTfhzk1j0U8dS_2BknBllC4b2NKp-OwcXatLTc/edit?usp=sharing SP-TSA 26-27]
|-
| Course materials || [[#Sources of Wisdom|Sources of Wisdom — notes, videos, assignments and reading]]
|-
| SP syllabus (ПУД) || [https://dp.hse.ru/#/summary?implementationId=16281511160 SP syllabus — 2025/26 archive]
|-
| TSA syllabus (ПУД) || [https://dp.hse.ru/#/summary?implementationId=16281526312 TSA syllabus — link from the 2026/27 course page]
|}

== Teaching team and contacts ==

'''Lecturers'''

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Name !! Teaching responsibilities !! Telegram
|-
| Пётр Лукьянченко || Lectures || [https://t.me/PetrLukianchenko @PetrLukianchenko]
|-
| Мария Кириллова || Lectures; seminars for groups 241, 242 || [https://t.me/makirill @makirill]
|-
| Алёна Числова || Lectures; seminars for groups 243, 244, 245 || [https://t.me/Alyona_Chislova @Alyona_Chislova]
|}

'''Teaching assistants'''

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Name !! Responsibilities / groups !! Telegram
|-
| Иван Новосад || Lecturer's assistant || [https://t.me/Mellodizzz @Mellodizzz]
|-
| Ева Каримова || Groups 243, 245 || [https://t.me/ekaesha @ekaesha]
|-
| Аскар Биктибаев || Group 244 || [https://t.me/askbkt @askbkt]
|-
| Ольга Макогонова || Groups 241, 242 || [https://t.me/komfajx @komfajx]
|}

== Rules of the game ==

=== Gradebook calculation ===

The calculation below follows the '''SP-TSA 26-27''' gradebook.

''Random processes, deterministic weights.''

<div style="padding:10px 16px; margin:14px 0;">
: '''Final (100-point scale) = round(0.25 × Q + 0.35 × M1 + 0.40 × M2*, 2).'''
: '''Final (10-point scale) = round(Final (100-point scale) / 10, 0).'''
</div>

Here Q is the aggregate quiz score, M1 is Scaled Mid1, and M2* is Scaled Mid2 unless a Scaled Retake score has been entered. When a retake score is present, it replaces Scaled Mid2.

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Item !! Rule
|-
| Quizzes 1–8 || Each quiz uses a 10-point scale. All eight quizzes count equally: Q = min(100, round((q1 + … + q8) / 8 × 10, 0)). No lowest scores are dropped.
|-
| Mid1 and Mid2 || The scores recorded in the gradebook use a 100-point scale and contribute 35% and 40%, respectively.
|-
| Retake || The entered Scaled Retake score replaces Scaled Mid2, including when it is lower. The quiz component and Scaled Mid1 are retained.
|-
| Rounding || Q is rounded to an integer before weighting. The weighted total is rounded to two decimal places, then divided by 10 and rounded to an integer. For nonnegative grades, halfway values round upwards: 6.5 becomes 7.
|-
| Incomplete results || The quiz result remains blank until at least one numeric quiz score is entered. The divisor remains 8, so unfilled quizzes contribute zero to the displayed calculation. The final grade remains blank until Q, Scaled Mid1 and either Scaled Mid2 or Scaled Retake are available.
|}

=== Stochastic Processes: Halloween, Ded Moroz and honey ===

In the course tradition, Mid1 is the '''Halloween Exam''' and Mid2 is the '''Ded Moroz Exam'''.

''Two seasonal encounters on the samurai path.''

'''Home assignments and quizzes'''

Home assignments are normally released weekly. You do not need to submit regular home assignments: the following class includes a quiz with one or two problems closely resembling the assignment. Once during the course, a home assignment takes the form of a computer-assisted project.

<div style="padding:12px 16px; margin:16px 0;">
'''Five honey pots''' — ''use wisely.''

At the end of the SP course, you have five honey pots, giving you the right to rewrite five missed or poorly completed quizzes. Honey pots consumed are recorded separately in the gradebook; this count is not a separate weighted component of the final grade.
</div>

These assignment and honey-pot rules are carried over from the [https://wiki.cs.hse.ru/Stochastic_processes_and_applications_DSBA_2025/2026 2025/26 SP course].

== Sources of Wisdom ==

''Enlightenment is not guaranteed. The reading list is a good start.''

The resources below come from earlier course pages. Assignment files and recordings retain their original academic-year labels.

=== Course materials ===

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Resource !! Links
|-
| Home assignments || [https://github.com/bdemeshev/hse_panda_stochastic_2025_fall/raw/main/home_assignments/home_assignments.pdf SP 2025/26] · [https://github.com/bdemeshev/hse_panda_tssp_2024_2025/raw/main/home_assignments/home_assignments.pdf SP–TSA 2024/25] · [https://raw.githubusercontent.com/bdemeshev/tssp_2023-24/main/ha/tssp_ha.pdf 2023/24]
|-
| Lecture and class notes || [https://disk.360.yandex.ru/d/ViBiodE8BPk2Aw 2025/26 notes] · [https://github.com/bdemeshev/hse_panda_tssp_2024_2025/tree/main/course_notes 2024/25 lecture slides and notes] · [https://disk.yandex.ru/d/5gs97BDSjwmOSw Maria Kirillova's notes — 2024/25]
|-
| Videos — made with love || [https://e.pcloud.link/publink/show?code=kZj5BOZAb8qNTSGI6LiGLeLWvMd4LMu4hsk 2025/26 recordings] · [https://e.pcloud.link/publink/show?code=kZDCKPZ6dPB3lXGHrhUzqeC7wkVfyaLsAq7 2024/25 class recordings] · [https://www.youtube.com/playlist?list=PLnIS95ct9auXMX-4-ESGZvigU1w6kexw0 Practice playlist]
|-
| Past exams || [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf Past exam collection] · [https://www.youtube.com/watch?v=dQw4w9WgXcQ Сливы осеннего экзамена 2026]
|-
| Course whitepaper || [https://github.com/bdemeshev/hse_panda_metrics_2024_2025/raw/main/whitepaper.pdf Whitepaper linked on the 2024/25 and 2025/26 course pages]
|}

=== Probability and Markov chains ===

* [https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf StoPro — Problems in Stochastic Processes]
* [https://projects.iq.harvard.edu/stat110/home Introduction to Probability — Blitzstein and Hwang]
* [https://www.statslab.cam.ac.uk/~rrw1/markov/M.pdf Cambridge lectures on Markov chains]
* [https://www.stat.berkeley.edu/~aldous/150/takis_exercises.pdf Takis Konstantopoulos — One Hundred Solved Exercises on Markov Chains]
* [https://aditya-sengupta.github.io/expository/markovtex.pdf Representing Markov Chains in LaTeX]
* [https://courses.cit.cornell.edu/econ620/reviewm6.pdf Convergence modes — Cornell review]
* [https://www.ee.iitb.ac.in/~sarva/courses/EE325/2014/Slides/ConvergenceOfRVs.pdf Convergence modes with examples — Saravanan Vijayakumaran]
* [https://github.com/mavam/stat-cookbook/releases/download/0.2.7/stat-cookbook.pdf Statistics Cookbook]

=== Monte Carlo and MCMC ===

* [https://eml.berkeley.edu/reprints/misc/understanding.pdf Chib and Greenberg — Understanding the Metropolis–Hastings Algorithm]
* [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Casella — Explaining the Gibbs Sampler]
* [https://projecteuclid.org/euclid.ps/1099928648 Roberts and Rosenthal — General State Space Markov Chains]
* [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]
* [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf Charles Geyer — MCMC lecture notes]

=== Stochastic calculus ===

* Zastawniak — ''Basic Stochastic Processes''.
* [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf ICEF master's course exams]
* [https://bdemeshev.github.io/sc401/ ICEF master's course notes — Russian]
* [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Draft textbook — Russian]
* [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Draft problem collection — Russian]

=== Time series ===

* [https://otexts.com/fpp3/ Forecasting: Principles and Practice — R]
* [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway and Stoffer — Time Series Analysis]
* [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay — teaching materials]
* [https://staff.fnwi.uva.nl/p.j.c.spreij/onderwijs/master/aadtimeseries2010.pdf Aad van der Vaart — Time Series lecture notes]
* [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Aad van der Vaart — Time Series course page]
* [https://github.com/bdemeshev/ts_pset Time series problem collection — Russian]

==== Unobserved components and state-space models ====

* [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Harvey–Jaeger example — Detrending, Stylized Facts and the Business Cycle]
* [https://core.ac.uk/download/pdf/6242335.pdf João Tovar Jalles — Structural Time Series Models and the Kalman Filter]
* [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey — Forecasting with Unobserved Components Models]
* [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton — state-space modelling]
* [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman — State Space Models]

=== Previous paths ===

* [https://wiki.cs.hse.ru/Stochastic_processes_and_applications_DSBA_2025/2026 2025/26 — Stochastic Processes and Applications]
* [https://wiki.cs.hse.ru/Tssp-2024-25 2024/25]
* [https://wiki.cs.hse.ru/Tssp-2023-24 2023/24]
* [https://wiki.cs.hse.ru/Tssp-2022-23 2022/23]
* [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 2021/22]
* [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 2020/21]

----
''The path continues, almost surely.''

Stochastic processes and applications DSBA 2026/2027

2026-09-08T03:44:01Z

Ianovosad: Первичная форма страницы

<div style="padding:0; margin:0 0 24px;">
<span style="font-size:90%; letter-spacing:2px;">FCS · HSE · 2026/27</span><br />
<span style="font-size:190%; line-height:1.25;">'''Stochastic Processes and Applications'''<br />'''Time Series Analysis'''</span><br />
<span>Data Science and Business Analytics</span>
</div>

<div style="border:1px solid #aaa; padding:12px 16px; margin:0 0 20px;">
<span lang="ja">侍には目標がなく道しかない</span><br />
''Samurai niwa mokuhyō ga naku michi shikanai.''<br />
'''A samurai has no goal, only a path.'''
</div>

''Random walks. Occasional enlightenment.''

<div style="padding:10px 14px; margin:16px 0;">
[https://t.me/+oM4HmZaMsV1lOWIy '''Course chat'''] · [[#Teaching team and contacts|Teaching team]] · [[#Rules of the game|Rules of the game]] · [[#Sources of Wisdom|Sources of Wisdom]]
</div>

''The chat may not work in the parking lot. The Markov property still does.''

== Course information ==

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Course !! Year of study !! Modules !! Credits !! Language
|-
| Stochastic Processes and Applications (SP) || 3 || 1–2 || 4 || English
|-
| Time Series Analysis (TSA) || 3 || 3–4 || 4 || English
|}

SP covers stochastic models, including Markov chains, random walks and martingales. TSA focuses on modelling and forecasting time series. Prerequisites include probability, statistical inference and linear algebra.

Official course information: [https://www.hse.ru/ba/data/courses/1163520553.html SP 2026/27] · [https://www.hse.ru/ba/data/courses/1163520577.html TSA 2026/27].

== Course links ==

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Resource !! Link
|-
| Telegram course chat || [https://t.me/+oM4HmZaMsV1lOWIy SP–TSA course chat]
|-
| Gradebook || SP-TSA 26-27
|-
| Course materials || [[#Sources of Wisdom|Sources of Wisdom — notes, videos, assignments and reading]]
|-
| SP syllabus (ПУД) || [https://dp.hse.ru/#/summary?implementationId=16281511160 SP syllabus — 2025/26 archive]
|-
| TSA syllabus (ПУД) || [https://dp.hse.ru/#/summary?implementationId=16281526312 TSA syllabus — link from the 2026/27 course page]
|}

== Teaching team and contacts ==

'''Lecturers'''

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Name !! Teaching responsibilities !! Telegram
|-
| Пётр Лукьянченко || Lectures || [https://t.me/PetrLukianchenko @PetrLukianchenko]
|-
| Мария Кириллова || Lectures; seminars for groups 241, 242 || [https://t.me/makirill @makirill]
|-
| Алёна Числова || Lectures; seminars for groups 243, 244, 245 || [https://t.me/Alyona_Chislova @Alyona_Chislova]
|}

'''Teaching assistants'''

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Name !! Responsibilities / groups !! Telegram
|-
| Иван Новосад || Lecturer's assistant || [https://t.me/Mellodizzz @Mellodizzz]
|-
| Ева Каримова || Groups 243, 245 || [https://t.me/ekaesha @ekaesha]
|-
| Аскар Биктибаев || Group 244 || [https://t.me/askbkt @askbkt]
|-
| Ольга Макогонова || Groups 241, 242 || [https://t.me/komfajx @komfajx]
|}

== Rules of the game ==

=== Gradebook calculation ===

The calculation below follows the '''SP-TSA 26-27''' gradebook.

''Random processes, deterministic weights.''

<div style="padding:10px 16px; margin:14px 0;">
: '''Final (100-point scale) = round(0.25 × Q + 0.35 × M1 + 0.40 × M2*, 2).'''
: '''Final (10-point scale) = round(Final (100-point scale) / 10, 0).'''
</div>

Here Q is the aggregate quiz score, M1 is Scaled Mid1, and M2* is Scaled Mid2 unless a Scaled Retake score has been entered. When a retake score is present, it replaces Scaled Mid2.

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Item !! Rule
|-
| Quizzes 1–8 || Each quiz uses a 10-point scale. All eight quizzes count equally: Q = min(100, round((q1 + … + q8) / 8 × 10, 0)). No lowest scores are dropped.
|-
| Mid1 and Mid2 || The scores recorded in the gradebook use a 100-point scale and contribute 35% and 40%, respectively.
|-
| Retake || The entered Scaled Retake score replaces Scaled Mid2, including when it is lower. The quiz component and Scaled Mid1 are retained.
|-
| Rounding || Q is rounded to an integer before weighting. The weighted total is rounded to two decimal places, then divided by 10 and rounded to an integer. For nonnegative grades, halfway values round upwards: 6.5 becomes 7.
|-
| Incomplete results || The quiz result remains blank until at least one numeric quiz score is entered. The divisor remains 8, so unfilled quizzes contribute zero to the displayed calculation. The final grade remains blank until Q, Scaled Mid1 and either Scaled Mid2 or Scaled Retake are available.
|}

=== Stochastic Processes: Halloween, Ded Moroz and honey ===

In the course tradition, Mid1 is the '''Halloween Exam''' and Mid2 is the '''Ded Moroz Exam'''.

''Two seasonal encounters on the samurai path.''

'''Home assignments and quizzes'''

Home assignments are normally released weekly. You do not need to submit regular home assignments: the following class includes a quiz with one or two problems closely resembling the assignment. Once during the course, a home assignment takes the form of a computer-assisted project.

<div style="padding:12px 16px; margin:16px 0;">
'''Five honey pots''' — ''use wisely.''

At the end of the SP course, you have five honey pots, giving you the right to rewrite five missed or poorly completed quizzes. Honey pots consumed are recorded separately in the gradebook; this count is not a separate weighted component of the final grade.
</div>

These assignment and honey-pot rules are carried over from the [https://wiki.cs.hse.ru/Stochastic_processes_and_applications_DSBA_2025/2026 2025/26 SP course].

== Sources of Wisdom ==

''Enlightenment is not guaranteed. The reading list is a good start.''

The resources below come from earlier course pages. Assignment files and recordings retain their original academic-year labels.

=== Course materials ===

{| class="wikitable" style="width:100%; border:1px solid #aaa;"
|-
! Resource !! Links
|-
| Home assignments || [https://github.com/bdemeshev/hse_panda_stochastic_2025_fall/raw/main/home_assignments/home_assignments.pdf SP 2025/26] · [https://github.com/bdemeshev/hse_panda_tssp_2024_2025/raw/main/home_assignments/home_assignments.pdf SP–TSA 2024/25] · [https://raw.githubusercontent.com/bdemeshev/tssp_2023-24/main/ha/tssp_ha.pdf 2023/24]
|-
| Lecture and class notes || [https://disk.360.yandex.ru/d/ViBiodE8BPk2Aw 2025/26 notes] · [https://github.com/bdemeshev/hse_panda_tssp_2024_2025/tree/main/course_notes 2024/25 lecture slides and notes] · [https://disk.yandex.ru/d/5gs97BDSjwmOSw Maria Kirillova's notes — 2024/25]
|-
| Videos — made with love || [https://e.pcloud.link/publink/show?code=kZj5BOZAb8qNTSGI6LiGLeLWvMd4LMu4hsk 2025/26 recordings] · [https://e.pcloud.link/publink/show?code=kZDCKPZ6dPB3lXGHrhUzqeC7wkVfyaLsAq7 2024/25 class recordings] · [https://www.youtube.com/playlist?list=PLnIS95ct9auXMX-4-ESGZvigU1w6kexw0 Practice playlist]
|-
| Past exams || [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf Past exam collection] · [https://www.youtube.com/watch?v=dQw4w9WgXcQ Сливы осеннего экзамена 2026]
|-
| Course whitepaper || [https://github.com/bdemeshev/hse_panda_metrics_2024_2025/raw/main/whitepaper.pdf Whitepaper linked on the 2024/25 and 2025/26 course pages]
|}

=== Probability and Markov chains ===

* [https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf StoPro — Problems in Stochastic Processes]
* [https://projects.iq.harvard.edu/stat110/home Introduction to Probability — Blitzstein and Hwang]
* [https://www.statslab.cam.ac.uk/~rrw1/markov/M.pdf Cambridge lectures on Markov chains]
* [https://www.stat.berkeley.edu/~aldous/150/takis_exercises.pdf Takis Konstantopoulos — One Hundred Solved Exercises on Markov Chains]
* [https://aditya-sengupta.github.io/expository/markovtex.pdf Representing Markov Chains in LaTeX]
* [https://courses.cit.cornell.edu/econ620/reviewm6.pdf Convergence modes — Cornell review]
* [https://www.ee.iitb.ac.in/~sarva/courses/EE325/2014/Slides/ConvergenceOfRVs.pdf Convergence modes with examples — Saravanan Vijayakumaran]
* [https://github.com/mavam/stat-cookbook/releases/download/0.2.7/stat-cookbook.pdf Statistics Cookbook]

=== Monte Carlo and MCMC ===

* [https://eml.berkeley.edu/reprints/misc/understanding.pdf Chib and Greenberg — Understanding the Metropolis–Hastings Algorithm]
* [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Casella — Explaining the Gibbs Sampler]
* [https://projecteuclid.org/euclid.ps/1099928648 Roberts and Rosenthal — General State Space Markov Chains]
* [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods]
* [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf Charles Geyer — MCMC lecture notes]

=== Stochastic calculus ===

* Zastawniak — ''Basic Stochastic Processes''.
* [https://github.com/bdemeshev/sc401/raw/master/matek2_collect/matek2_collection.pdf ICEF master's course exams]
* [https://bdemeshev.github.io/sc401/ ICEF master's course notes — Russian]
* [https://github.com/bdemeshev/sc_book/raw/master/sc_book.pdf Draft textbook — Russian]
* [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Draft problem collection — Russian]

=== Time series ===

* [https://otexts.com/fpp3/ Forecasting: Principles and Practice — R]
* [https://www.stat.pitt.edu/stoffer/tsa4/ Shumway and Stoffer — Time Series Analysis]
* [https://faculty.chicagobooth.edu/ruey-s-tsay/teaching Ruey Tsay — teaching materials]
* [https://staff.fnwi.uva.nl/p.j.c.spreij/onderwijs/master/aadtimeseries2010.pdf Aad van der Vaart — Time Series lecture notes]
* [http://www.math.leidenuniv.nl/~avdvaart/timeseries/index.html Aad van der Vaart — Time Series course page]
* [https://github.com/bdemeshev/ts_pset Time series problem collection — Russian]

==== Unobserved components and state-space models ====

* [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html Harvey–Jaeger example — Detrending, Stylized Facts and the Business Cycle]
* [https://core.ac.uk/download/pdf/6242335.pdf João Tovar Jalles — Structural Time Series Models and the Kalman Filter]
* [https://pdfs.semanticscholar.org/0bc8/582016086017763b93e87ad8640ec1816aeb.pdf Harvey — Forecasting with Unobserved Components Models]
* [http://www.chadfulton.com/fulton_statsmodels_2017/ Chad Fulton — state-space modelling]
* [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman — State Space Models]

=== Previous paths ===

* [https://wiki.cs.hse.ru/Stochastic_processes_and_applications_DSBA_2025/2026 2025/26 — Stochastic Processes and Applications]
* [https://wiki.cs.hse.ru/Tssp-2024-25 2024/25]
* [https://wiki.cs.hse.ru/Tssp-2023-24 2023/24]
* [https://wiki.cs.hse.ru/Tssp-2022-23 2022/23]
* [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 2021/22]
* [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 2020/21]

----
''The path continues, almost surely.''