Diffusion Models for Time Series: Review the Model

Topics: Review the model Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.

Understand models to estimate conditional probabilities

In Progress

Diffusion Models for Time Series: Review the Model

Topics: Review the model Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.

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- Everyone shall get their chance to lead the discussion.
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Conditional probability estimation is one of the most fundamental problems in statistics.

- Conditional probability estimation is frequently used in solving both real life and academic problems. One is likely to encounter this problem at some point of their life.
- If you are inferring, you are probably using conditional probabilities. It is a perspective.
- There are many models and methods to estimate the conditional probability. We can learn about and from these models and methods.
- We need a universal model to solve this problem for productivity. A universal model for this task will save us a lot of time and energy.
- Many machine learning methods are based on conditional probabilities.
- Many classifiers
- Bayesian networks
- …

- Read and Discuss
- Apply on toy problems

We will update this list on our way forward. Here is a partial list of references.

As a start this is an outline of what should be covered.

- What is the conditional probability?
- Sampling theory
- Bayes
- Representation of a conditional probability

- Statistical methods to estimate the conditional probability
- The list is enormous. We will only concentrate on the basics.

- Tree-based
- Tree as “clustering” method
- Application on the bike-sharing problem

- NN-based
- NN as feature transformations
- Application on the bike-sharing problem

- EM Methods
- Variational Methods
- Normalizing Flow
- To be added as we learn more about it

We have prepared dataset that can be used both for classification problems and regression problems.

- Timezone conversions: World Clock

Conditional Probability Estimation

- Conditional Probability and Bayes
- Least Squares, Bootstrap, Maximum Likelihood, and Bayesian
- EM Methods
- Variantional Inference Normalizing Flow
- Review of Normalizing Flow
- Deep AutoRegressive Networks
- MADE: Masked Autoencoder for Distribution Estimation
- MAF: how is MADE being used
- Summary of Generative Models
- Energy-based Models
- Energy-based Models 2
- Energy-based Models 3
- Energy-based Models 4
- Energy-based Models 5
- Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
- LTD/LTP
- Self-supervised Learning: Generative or Contrastive
- Self-supervised Learning: GAN
- Self-supervised Learning: Theories (Part 1)
- Self-supervised Learning: Theories (Part 2)
- Graph Neural Networks: Basics
- Graph Neural Networks: Basics (2)
- Graph Neural Networks
- Graph Neural Networks: PyTorch
- Graph Neural Networks: Theoretical Motivations
- Graph Neural Networks: Theoretical Motivations (Part 2)
- Graph Convolutional Matrix Completion
- Multivariate Time-series Forecasting Using GNN
- Hamilton WL. Graph Representation Learning. Chapter 8
- Hamilton WL. Graph Representation Learning. Chapter 8 (2)
- Uncertainty in Deep Learning
- Causal Inference
- Counterfactual Explanation in Multivariate Time Series
- Review of Time Series Forecasting
- Conformal Time Series Forecasting
- Evaluating time series forecasting models
- DeepAR
- Temporal Fusion Transformer
- Data Augmentation for Time Series
- M Competition
- Neural ODE
- Gradient Boosted Decision Trees (I)
- Gradient Boosted Decision Trees (II)
- Forecasting with Trees
- Probabilistic Forecasting: A Level-Set Approach
- Diffusion Models: A Comprehensive Survey of Methods and Applications
- Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
- End of 2022 Fireside Chat
- GitHub Actions for Data Scientists
- Diffusion Models for Time Series: Session 1
- Diffusion Models for Time Series: the Paper
- Diffusion Models for Time Series: Initiation
- Diffusion Models for Time Series: Data
- Diffusion Models for Time Series: Dataloader
- Diffusion Models for Time Series: Dataloader and Collation
- Diffusion Models for Time Series: Dataloader Discussions and Next Steps
- Diffusion Models for Time Series: Review the Model
- Inferring causal impact using Bayesian structural time-series models
- References for Probability Estimation Club