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docs: update README;
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WenjieDu committed Apr 9, 2024
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69 changes: 36 additions & 33 deletions README.md
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Expand Up @@ -192,39 +192,42 @@ The paper references are all listed at the bottom of this readme file. Please re
🌟 Since **v0.2**, all neural-network models in PyPOTS has got hyperparameter-optimization support.
This functionality is implemented with the [Microsoft NNI](https://github.com/microsoft/nni) framework.

| ***`Imputation`*** | 🚥 | 🚥 | 🚥 |
|:----------------------:|:-----------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
| **Type** | **Abbr.** | **Full name of the algorithm/model** | **Year** |
| Neural Net | SAITS | Self-Attention-based Imputation for Time Series [^1] | 2023 |
| Neural Net | Transformer | Attention is All you Need [^2];<br>Self-Attention-based Imputation for Time Series [^1];<br><sub>Note: proposed in [^2], and re-implemented as an imputation model in [^1].</sub> | 2017 |
| Neural Net | Crossformer | Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting [^16] | 2023 |
| Neural Net | TimesNet | Temporal 2D-Variation Modeling for General Time Series Analysis [^14] | 2023 |
| Neural Net | PatchTST | A Time Series is Worth 64 Words: Long-Term Forecasting with Transformers [^18] | 2023 |
| Neural Net | DLinear | Are Transformers Effective for Time Series Forecasting? [^17] | 2023 |
| Neural Net | ETSformer | Exponential Smoothing Transformers for Time-series Forecasting [^19] | 2023 |
| Neural Net | FEDformer | Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [^20] | 2022 |
| Neural Net | Informer | Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [^21] | 2021 |
| Neural Net | Autoformer | Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [^15] | 2021 |
| Neural Net | CSDI | Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [^12] | 2021 |
| Neural Net | US-GAN | Unsupervised GAN for Multivariate Time Series Imputation [^10] | 2021 |
| Neural Net | GP-VAE | Gaussian Process Variational Autoencoder [^11] | 2020 |
| Neural Net | BRITS | Bidirectional Recurrent Imputation for Time Series [^3] | 2018 |
| Neural Net | M-RNN | Multi-directional Recurrent Neural Network [^9] | 2019 |
| Naive | LOCF/NOCB | Last Observation Carried Forward / Next Observation Carried Backward | - |
| Naive | Median | Median Value Imputation | - |
| Naive | Mean | Mean Value Imputation | - |
| ***`Classification`*** | 🚥 | 🚥 | 🚥 |
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| Neural Net | BRITS | Bidirectional Recurrent Imputation for Time Series [^3] | 2018 |
| Neural Net | GRU-D | Recurrent Neural Networks for Multivariate Time Series with Missing Values [^4] | 2018 |
| Neural Net | Raindrop | Graph-Guided Network for Irregularly Sampled Multivariate Time Series [^5] | 2022 |
| ***`Clustering`*** | 🚥 | 🚥 | 🚥 |
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| Neural Net | CRLI | Clustering Representation Learning on Incomplete time-series data [^6] | 2021 |
| Neural Net | VaDER | Variational Deep Embedding with Recurrence [^7] | 2019 |
| ***`Forecasting`*** | 🚥 | 🚥 | 🚥 |
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| Probabilistic | BTTF | Bayesian Temporal Tensor Factorization [^8] | 2021 |
🔥 Note that Transformer, Crossformer, PatchTST, DLinear, ETSformer, FEDformer, Informer, Autoformer are not proposed as imputation methods in their original papers,
and they cannot accept POTS as input. **To make them applicable on POTS data, we apply the embedding strategy the same as we did in [SAITS paper](https://arxiv.org/pdf/2202.08516).**

| ***`Imputation`*** | 🚥 | 🚥 | 🚥 |
|:----------------------:|:-----------:|:-----------------------------------------------------------------------------------------------:|:--------:|
| **Type** | **Abbr.** | **Full name of the algorithm/model** | **Year** |
| Neural Net | SAITS | Self-Attention-based Imputation for Time Series [^1] | 2023 |
| Neural Net | Transformer | Attention is All you Need [^2] | 2017 |
| Neural Net | Crossformer | Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting [^16] | 2023 |
| Neural Net | TimesNet | Temporal 2D-Variation Modeling for General Time Series Analysis [^14] | 2023 |
| Neural Net | PatchTST | A Time Series is Worth 64 Words: Long-Term Forecasting with Transformers [^18] | 2023 |
| Neural Net | DLinear | Are Transformers Effective for Time Series Forecasting? [^17] | 2023 |
| Neural Net | ETSformer | Exponential Smoothing Transformers for Time-series Forecasting [^19] | 2023 |
| Neural Net | FEDformer | Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [^20] | 2022 |
| Neural Net | Informer | Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [^21] | 2021 |
| Neural Net | Autoformer | Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [^15] | 2021 |
| Neural Net | CSDI | Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [^12] | 2021 |
| Neural Net | US-GAN | Unsupervised GAN for Multivariate Time Series Imputation [^10] | 2021 |
| Neural Net | GP-VAE | Gaussian Process Variational Autoencoder [^11] | 2020 |
| Neural Net | BRITS | Bidirectional Recurrent Imputation for Time Series [^3] | 2018 |
| Neural Net | M-RNN | Multi-directional Recurrent Neural Network [^9] | 2019 |
| Naive | LOCF/NOCB | Last Observation Carried Forward / Next Observation Carried Backward | - |
| Naive | Median | Median Value Imputation | - |
| Naive | Mean | Mean Value Imputation | - |
| ***`Classification`*** | 🚥 | 🚥 | 🚥 |
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| Neural Net | BRITS | Bidirectional Recurrent Imputation for Time Series [^3] | 2018 |
| Neural Net | GRU-D | Recurrent Neural Networks for Multivariate Time Series with Missing Values [^4] | 2018 |
| Neural Net | Raindrop | Graph-Guided Network for Irregularly Sampled Multivariate Time Series [^5] | 2022 |
| ***`Clustering`*** | 🚥 | 🚥 | 🚥 |
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| Neural Net | CRLI | Clustering Representation Learning on Incomplete time-series data [^6] | 2021 |
| Neural Net | VaDER | Variational Deep Embedding with Recurrence [^7] | 2019 |
| ***`Forecasting`*** | 🚥 | 🚥 | 🚥 |
| **Type** | **Abbr.** | **Full name of the algorithm/model/paper** | **Year** |
| Probabilistic | BTTF | Bayesian Temporal Tensor Factorization [^8] | 2021 |


## ❖ Citing PyPOTS
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2 changes: 1 addition & 1 deletion pypots/__init__.py
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#
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
__version__ = "0.3.2"
__version__ = "0.4"


from . import imputation, classification, clustering, forecasting, optim, data, utils
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