Conditional Probability Estimation

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.

Current Topic

Our Current Topic is Deep Learning for Time Series.

Here is a preliminary list of papers to be discussed.

Topics	Paper
Review	Time Series Forecasting With Deep Learning: A Survey
Review	“Gneiting T, Katzfuss M. Probabilistic Forecasting. Annu Rev Stat Appl. 2014;1: 125–151. doi:10.1146/annurev-statistics-062713-085831”
Uncertainty	Conformal Time-series Forecasting
Uncertainty	Probabilistic Forecasting: A Level Set Approach
Probabilistic	Autoregressive Dernoising Diffusion Models for Mutivariate Probablistic Forecasting
Probabilistic	DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Probabilistic	Probabilistic Time Series Forecasting with Implicit Quantile Networks
Probabilistic	Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Probabilistic	Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
Probabilistic	Deep Factors for Forecasting
Transformer	(TFT) Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
AE	Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
?	N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Causal Inference	Causal Inference for Time series Analysis: Problems, Methods and Evaluation
Evaluation	Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability
Evaluation	An introduction to multivariate probabilistic forecast evaluation
Evaluation	Evaluating Probabilistic Forecasts with scoringRules , Scoring Rules for Continuous Probability Distributions
Traditional Method	Forecasting: Principles and Practice
Preprocessing	Time Series Data Augmentation for Deep Learning: A Survey

When and How

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The discussions are mostly in Chinese.

When

This is a bi-weekly meetup.

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Rules

Everyone shall get their chance to lead the discussion.
The first principle is to understand the content. Interrupt and ask any questions to make sure we all understand the content well.

Why this Topic

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
- …

What is Our Approach

Read and Discuss
Apply on toy problems

Reading List and References

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

Initial Proposal (Outdated)

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

oy Problems (Outdated)

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

Bike-sharing data

Tools

Timezone conversions: World Clock

Conditional Probability Estimation