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Projects & Frameworks

Here we present some potential project ideas:

For the semester project - you can bring your own data - we will discuss this in the first/second session. Nevertheless here you can find some resources to think about a potential project:

Check out these frameworks:

Week 5 - Dependence concepts: Copula // Gaussian Processes // RMT

Continue working on your semester project !!!

Try to answer/motivate these questions:

What is you idea/topic?

Which data do you use?

How is it structured?

Is there something special you want to forecast?

OPTIONAL:

Week 6 - Extremes // Anomalies // Signatures

Continue working on your semester project !!!

OPTIONAL --- If you need to brush-up your knowledge on Trees:

Week 9 - Transformers + TemporalFusionTransformers

Finalize your presentation !!!

Finalize your semester project !!!

OPTIONAL:

Week 3 - Labour Day

Work on your semester project !!!

Plot your data... ;-)

Think about an appropiate approach for your problem

Formulate some questions about things you do not know

OPTIONAL:

Week 13 - Final Presentations (Back-Up)

Finalize your semester project !!!

CONGRATULATIONS - YOU MADE IT !!!

References / Books

YES.....books.....you may heard about it....these things were used in former times ;-)

Last but not least - here are some very good book references. It is not required or requested to buy any of these books. If you are enrolled as a student at CAU, FH Kiel or another university you might have access to these books via the SpringerLink of your home institution. They cover the course material quite well and provide GitHub-Repositories for the codes which where used throughout the book. Unfortunately there is no 1:1-Coursera course matching these books perfectly - but we will do our best ;-)

Some additional course material and MOOCs if you want you expand your knowledge at your own pace:

Week 4 - State-Space models // Filtering

Continue working on your semester project !!!

Define an appropriate approach for your problem

Formulate some questions about things you do not know

OPTIONAL:

Week 2 - SARIMA(X) + GARCH-Models

Watch the second YouTube-Tutorial:

Work on your semester project topic and get started.

This means:

Connect to your team members

Launch a GitHub Repository

Prepare your data

OPTIONAL:

Lecture material + YouTube

For the summer-term 2024 we use mainly YouTube playlists and freely available resources.

Check out the more detailed playlists in each week.

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In case you enjoy Coursera - here are some suggestions:

  • register with Google and the Coursera platform to get corresponding accounts. Since we switched for the second/third run our video lecture sources (the Coursera part is optional - but still benefical for the interested) this part is optional.

Please enroll/attend/complete the following Coursera MOOCs during the semester:

Week 1 - Intro + Organisation

Think about your project:

What are your skills and interests?

What motivates you?

Watch the first YouTube-Tutorials

THIS PLAYLIST IS OPTIONAL:

Week 10 - NBEATS(x) + NHITS

Finalize your presentation !!!

Finalize your semester project !!!

OPTIONAL:

Week 7 - Tree models: XGBoost // LightGBM // CatBoost

Continue working on your semester project !!!

Requirements for a Certificate of Achievement or ECTS

The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:

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Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

Please switch on your camera - ask questions - be (inter)-active!!

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Projects:

Check the Projects section to learn more about the projects.

Advanced Time Series Prediction

Hi everybody......it´s Kristian - your course instructor. I will guide you through the "Advanced Time Series Prediction"-course this semester.

Welcome to all participants.

We start with a short introduction of the course topics. Get to know each other and explain the course structure and all the required tasks for a successful participation.

This requires i.e.: Give a project presentation and provide a well documented code with data via GitHub. Addtionally you are only allowed to miss two class sessions during the semester.

Data Products | Cboe DataShopdatashop.cboe.comchevron-right
FINANCE
GitHub - ranaroussi/yfinance: Download market data from Yahoo! Finance's APIGitHubchevron-right
FINANCE
Home - investpyinvestpy.readthedocs.iochevron-right
FINANCE
Hannover | Wetterrückblick & Klimadaten | MeteostatMeteostatchevron-right
WEATHER
Energy-Chartswww.energy-charts.infochevron-right
ENERGY
DatasetsECMWFchevron-right
ENVIRONMENT
GitHub - javedali99/python-resources-for-earth-sciences: A Curated List of Python Resources for Earth SciencesGitHubchevron-right
ENVIRONMENT
GitHub - tymefighter/ForecastGitHubchevron-right
ICEwww.ice.comchevron-right
FINANCE
Kenneth R. French - Data Librarymba.tuck.dartmouth.educhevron-right
ECONOMICS
statsmodels 0.14.6www.statsmodels.orgchevron-right
GitHub - mortada/fredapi: Python API for FRED (Federal Reserve Economic Data) and ALFRED (Archival FRED)GitHubchevron-right
ECONOMICS
Time Series Classification Websitewww.timeseriesclassification.comchevron-right
MEDICAL
excess-mortality/all-countries.ipynb at main · dkobak/excess-mortalityGitHubchevron-right
MEDICAL
Analyze Geospatial Data in Python: GeoPandas and Shapelywww.learndatasci.comchevron-right
ENVIRONMENT
PEGELONLINEwww.pegelonline.wsv.dechevron-right
ENVIRONMENT
Graphs and mapsEUROMOMOchevron-right
MEDICAL
Introduction — PyFlux 0.4.7 documentationpyflux.readthedocs.iochevron-right
arch 7.2.0arch.readthedocs.iochevron-right

Week 8 - (Deep) recurrent architectures for time series data

Continue working on your semester project !!!

OPTIONAL:

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PyTorch Forecasting Documentation — pytorch-forecasting documentationpytorch-forecasting.readthedocs.iochevron-right

Week 11 - LLM for time series problems

Finalize your presentation !!!

Finalize your semester project !!!

OPTIONAL:

GluonTS documentationts.gluon.aichevron-right
Welcome to skforecast - Skforecast Docsskforecast.orgchevron-right
Time Series Made Easy in Python — darts documentationunit8co.github.iochevron-right
Model References — HyperTS 0.1.4 documentationhyperts.readthedocs.iochevron-right
Nixtla | State of the Art ForecastingNixtlachevron-right
Course Detail | NVIDIAlearn.nvidia.comchevron-right
Media Hopper Createmedia_hopperchevron-right
Time Series Analysis by State Space MethodsOUP Academicchevron-right
https://link.springer.com/book/10.1007/978-1-4842-7150-6link.springer.comchevron-right
https://www.youtube.com/playlist?list=PLblh5JKOoLULU0irPgs1SnKO6wqVjKUsQarrow-up-right
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https://www.youtube.com/playlist?list=PLmZlBIcArwhMJoGk5zpiRlkaHUqy5dLzLarrow-up-right
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https://www.youtube.com/watch?v=_K1OsqCicBYarrow-up-right
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-TC-01+V1arrow-up-right
https://www.youtube.com/watch?v=sa7E9qUHYXwarrow-up-right
https://www.youtube.com/watch?v=WMOrCBxxgvAarrow-up-right
https://media.ed.ac.uk/channel/Probability++Estimation+Theory+and++Random+Signals+%28PETARS%29/174860421arrow-up-right
https://www.youtube.com/playlist?list=PLMrJAkhIeNNQkv98vuPjO2X2qJO_UPeWRarrow-up-right
https://www.youtube.com/watch?v=s2K1JfNR7Scarrow-up-right
https://www.youtube.com/playlist?list=PLMrJAkhIeNNT_Xh3Oy0Y4LTj0Oxo8GqsCarrow-up-right
https://www.youtube.com/playlist?list=PLMrJAkhIeNNSVXUvppZTYNHKQUD-oWys9arrow-up-right
https://www.youtube.com/watch?v=Gka11q5VfFIarrow-up-right
https://academic.oup.com/book/16563arrow-up-right
Iain Explains Signals, Systems, and Digital CommsYouTubechevron-right
Towards Data ScienceTowards Data Sciencechevron-right
https://github.com/Apress/advanced-forecasting-pythonarrow-up-right
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https://www.youtube.com/@iain_explains/playlistsarrow-up-right
https://www.youtube.com/playlist?list=PL6Vi_EcJpt8E96_JTKoOKY3HYWVGjf6b4arrow-up-right
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Machine Learning for Time-Series with PythonPacktchevron-right
https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Pythonarrow-up-right
https://link.springer.com/book/10.1007/978-1-4842-5992-4link.springer.comchevron-right
Time Series Analysis with Python CookbookPacktchevron-right
https://link.springer.com/book/10.1007/978-1-4842-8978-5link.springer.comchevron-right
https://link.springer.com/book/10.1007/978-3-031-13584-2link.springer.comchevron-right
Time Series Forecasting in PythonManning Publicationschevron-right
Practical Time Series AnalysisCourserachevron-right
This is a nice time series course with a focus on R/RStudio. It goes slightly deeper then our first course but lacks machine learning techniques. Nevertheless a very good ranked course and worth doing it - if you like to deepen your knowledge.
Forecasting: Principles and Practice (2nd ed)robjhyndmanchevron-right
This book is freely available and provides additional information. Focus on R/RStudio.
Modern Time Series Forecasting with PythonPacktchevron-right
Practical Time Series AnalysisCourserachevron-right
Enroll and complete this course in Coursera
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https://www.youtube.com/watch?v=Zyj8iFSK7xYarrow-up-right
https://www.youtube.com/watch?v=L-hRexVa32karrow-up-right

Week 12 - Final Presentations

Finalize your semester project !!!

CONGRATULATIONS - YOU MADE IT !!!

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https://www.youtube.com/playlist?list=PLPjEEUWIWhQV7X6dXfrVP3w0KBBLBVJ0jarrow-up-right
https://www.youtube.com/playlist?list=PLJYjjnnccKYDppALiJlHskU8md904FXgdarrow-up-right
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https://www.youtube.com/watch?v=B_a0K2Uj85Qarrow-up-right
https://www.youtube.com/watch?v=xcbj0RE3kfIarrow-up-right
https://github.com/Apress/hands-on-time-series-analylsis-pythonarrow-up-right
https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbookarrow-up-right
https://github.com/Apress/time-series-algorithm-recipesarrow-up-right
https://github.com/QuantLet/pyTSA/arrow-up-right
https://github.com/marcopeix/TimeSeriesForecastingInPython/tree/masterarrow-up-right
https://github.com/PacktPublishing/Modern-Time-Series-Forecasting-with-Pythonarrow-up-right
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Time Series Forecasting using Deep LearningBPB Onlinechevron-right
https://github.com/bpbpublications/Time-Series-Forecasting-using-Deep-Learningarrow-up-right
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V-Lab: Volatility Analysis DocumentationV-Labchevron-right
GitHub - iankhr/armagarch: ARMA-GARCHGitHubchevron-right
V-Lab: Correlation Analysis DocumentationV-Labchevron-right
DCC-GARCH/examples/dcc_garch_modeling.ipynb at master · Topaceminem/DCC-GARCHGitHubchevron-right
https://www.youtube.com/playlist?list=PLzAfHlPtM1I4bPhNE5FKOi0S7erZqm5pgarrow-up-right
https://www.youtube.com/playlist?list=PLvcbYUQ5t0UHOLnBzl46_Q6QKtFgfMGc3arrow-up-right
https://www.youtube.com/playlist?list=PLzAfHlPtM1I7jej6_SxBAbBR-XQM0SBn0arrow-up-right
Forecasting Volatility: Deep Dive into ARCH & GARCH ModelsMediumchevron-right
Multivariate GARCH with Python and Tensorflow – Sarem Seitzsarem-seitz.comchevron-right
https://www.youtube.com/playlist?list=PLtIY5kwXKny91_IbkqcIXuv6t1prQwFhOarrow-up-right
GitHub - vishnukanduri/Time-series-analysis-in-Python: I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.GitHubchevron-right
https://www.youtube.com/playlist?list=PL0vEWJI_pj7SWa-cOUZZlHtpMQ7ULrTdYarrow-up-right
app_deep_learning/t81_558_class_10_1_timeseries.ipynb at main · jeffheaton/app_deep_learningGitHubchevron-right
https://www.youtube.com/watch?v=L8HKweZIOmgarrow-up-right
Transformers Explained Visually (Part 1): Overview of Functionality | Towards Data ScienceTowards Data Sciencechevron-right
https://www.youtube.com/watch?v=PSs6nxngL6karrow-up-right
https://www.youtube.com/watch?v=viZrOnJclY0arrow-up-right
Specialized Models: Time Series and Survival AnalysisCourserachevron-right
Enroll and complete this course in Coursera
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