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    • Introduction to Data Science and Machine Learningchevron-right
    • Machine Learning with TensorFlowchevron-right
    • Intermediate Machine Learningchevron-right
    • From LLMs to AI Agents🤖chevron-right
    • Advanced Time Series Predictionchevron-right
    • Python: Beginner to Practitionerchevron-right
    • Fine-Tuning and Deployment of Large Language Modelschevron-right
    • Archivechevron-right
      • Deep Learning from Scratchchevron-right
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      • Application of Transformer Modelschevron-right
      • Generative Adversarial Networkschevron-right
      • Lehren und Lernen mit KIchevron-right
      • Reinforcement Learning
      • Machine Learning Operations (MLOps)chevron-right
      • Mathematik für maschinelles Lernen
      • TensorFlow Course: Week 10 - Special Issues Considering Your Final Projects
      • Deep Dive into LLMschevron-right
      • Intermediate Machine Learning (Legacy SS2023)chevron-right
      • Practical Engineering with LLMschevron-right
      • Python: From Beginner to Practictioner (Legacy WS2023)chevron-right
      • Machine Learning für die Medizinchevron-right
      • Time Series Predictionchevron-right
        • Requirements for a Certificate of Achievement or ECTS
        • Projects & Frameworks
        • Preparation / YouTube
        • References / Books
        • Week 1 - Intro + Organisation
        • Week 2 - Forecasting basics with trends: AR + MA-models
        • Week 3 - Covering seasonality: From ARMA to SARIMA-models
        • Week 4 - Towards multidimensional settings: SARIMAX + VAR-models
        • Week 5 - Non-Stationary model classes: GARCH + DCC-GARCH
        • Week 6 - Copula Methods
        • Week 7 - Milestone Meeting + Spectral Analysis of Time Series + Kalman-Filtering
        • Week 8 - Supervised Learning I: Trees + Random Forests + Boosting
        • Week 9 - Supervised Learning II: XGBoost + LightGBM + CatBoost
        • Week 10 - Neural Networks for Sequences: RNNs + GRUs + LSTMs + LMUs
        • Week 11 - Prophet(Facebook) + DeepAR(Amazon) + GPVAR
        • Week 12 - Transformers + TFTs
        • Week 13 - NBEATS(s) + NHITS(x)
        • Week 14 - Final Presentation
      • Python: From Beginner to Practitioner (Legacy 2024S)chevron-right
      • Einführung in Data Science und maschinelles Lernenchevron-right
      • Python: From Beginner to Practitioner (Legacy 2024W)chevron-right
  • Events
    • Coding.Waterkant 2023
    • Prototyping Week
  • Course Projects
    • Choosing a Project
    • How to Start, Complete, and Submit Your Project
  • Additional Resourses
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    • Coursera
    • Selecting the Optimizer
    • Choosing the Learning Rate
    • Learning Linear Algebra
    • Learning Python
    • Support Vector Machines
    • ML Statistics
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    • Google Colab
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block-quoteOn this pagechevron-down
  1. Courseschevron-right
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  3. Time Series Prediction

Week 11 - Prophet(Facebook) + DeepAR(Amazon) + GPVAR

Try out the Prophet/DeepAR tutorials which was recommended. Try to answer/prepare the homework problems.

Finalize your semester project !!!

Check-Out these links:

LogoMultiple Time Series Forecasting with DeepAR in PythonForecastegychevron-right
A Visual Exploration of Gaussian ProcessesDistillchevron-right
LogoGaussian processes (1/3) - From scratchPeter’s Noteschevron-right
LogoGaussian processes (2/3) - Fitting a Gaussian process kernelPeter’s Noteschevron-right
LogoGaussian processes (3/3) - exploring kernelsPeter’s Noteschevron-right
https://nbviewer.org/github/adamian/adamian.github.io/blob/master/talks/Brown2016.ipynbnbviewer.orgchevron-right

http://adamian.github.io/talks/Damianou_GP_tutorial.htmlarrow-up-right

https://jovian.com/nkafr/deepvararrow-up-right

LogoDeep GPVAR: Upgrading DeepAR For Multi-Dimensional Forecasting | Towards Data ScienceTowards Data Sciencechevron-right
PreviousWeek 10 - Neural Networks for Sequences: RNNs + GRUs + LSTMs + LMUschevron-leftNextWeek 12 - Transformers + TFTschevron-right

Last updated 1 year ago

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