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.

ReviewTime 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”
UncertaintyConformal Time-series Forecasting
UncertaintyProbabilistic Forecasting: A Level Set Approach
ProbabilisticAutoregressive Dernoising Diffusion Models for Mutivariate Probablistic Forecasting
ProbabilisticDeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
ProbabilisticProbabilistic Time Series Forecasting with Implicit Quantile Networks
ProbabilisticMultivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
ProbabilisticProbabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
ProbabilisticDeep Factors for Forecasting
Transformer(TFT) Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
AERecurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
?N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Causal InferenceCausal Inference for Time series Analysis: Problems, Methods and Evaluation
EvaluationRandom Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability
EvaluationAn introduction to multivariate probabilistic forecast evaluation
EvaluationEvaluating Probabilistic Forecasts with scoringRules , Scoring Rules for Continuous Probability Distributions
Traditional MethodForecasting: Principles and Practice
PreprocessingTime Series Data Augmentation for Deep Learning: A Survey

When and How

The discussions are hosted online in Lark/Wechat.

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The discussions are mostly in Chinese.


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  • 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.


Conditional Probability Estimation