opencampus.sh Machine Learning Program
CtrlK
  • opencampus.sh Machine Learning Program
  • Course Kick-Off
  • How do I choose a course?
  • FAQ
  • Courses
    • Introduction to Data Science and Machine Learning
    • Machine Learning with TensorFlow
    • Intermediate Machine Learning
    • From LLMs to AI Agents🤖
    • Advanced Time Series Prediction
    • Python: Beginner to Practitioner
    • Fine-Tuning and Deployment of Large Language Models
    • Archive
      • Deep Learning from Scratch
      • Deep Learning for Computer Vision
      • Application of Transformer Models
      • Generative Adversarial Networks
      • Lehren und Lernen mit KI
      • Reinforcement Learning
      • Machine Learning Operations (MLOps)
      • Mathematik für maschinelles Lernen
      • TensorFlow Course: Week 10 - Special Issues Considering Your Final Projects
      • Deep Dive into LLMs
      • Intermediate Machine Learning (Legacy SS2023)
      • Practical Engineering with LLMs
      • Python: From Beginner to Practictioner (Legacy WS2023)
      • Machine Learning für die Medizin
      • Time Series Prediction
        • 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)
      • Einführung in Data Science und maschinelles Lernen
      • Python: From Beginner to Practitioner (Legacy 2024W)
  • Events
    • Coding.Waterkant 2023
    • Prototyping Week
  • Course Projects
    • Choosing a Project
    • How to Start, Complete, and Submit Your Project
  • Additional Resourses
    • Glossary
    • Coursera
    • Selecting the Optimizer
    • Choosing the Learning Rate
    • Learning Linear Algebra
    • Learning Python
    • Support Vector Machines
    • ML Statistics
  • Tools
    • Git
    • RStudio
    • Google Colab
    • Zoom
Powered by GitBook
On this page

Was this helpful?

  1. Courses
  2. Archive
  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 PythonForecastegy
A Visual Exploration of Gaussian ProcessesDistill
LogoGaussian processes (1/3) - From scratchPeter’s Notes
LogoGaussian processes (2/3) - Fitting a Gaussian process kernelPeter’s Notes
LogoGaussian processes (3/3) - exploring kernelsPeter’s Notes
https://nbviewer.org/github/adamian/adamian.github.io/blob/master/talks/Brown2016.ipynbnbviewer.org

http://adamian.github.io/talks/Damianou_GP_tutorial.html

https://jovian.com/nkafr/deepvar

LogoDeep GPVAR: Upgrading DeepAR For Multi-Dimensional Forecasting | Towards Data ScienceTowards Data Science
PreviousWeek 10 - Neural Networks for Sequences: RNNs + GRUs + LSTMs + LMUsNextWeek 12 - Transformers + TFTs

Last updated 1 year ago

Was this helpful?