fix: allocate CRPS accumulator on the input device

.github/workflows/greetings.yml

@@ -3,7 +3,7 @@ name: Auto reply to new issues and PR
 on:
   issues:
     types: [opened]
-  pull_request:
+  pull_request_target:
     branches: [main, dev]
     types: [opened]
 

.github/workflows/stale.yml

@@ -1,22 +1,32 @@
-name: "Close stale issues and PRs"
+name: "Stale Manager"
 
 on:
+  push:
+    branches:
+      - test_stale_manager
   schedule:
-    - cron: "0 0 * * *"
+    - cron: "0 8 * * *"
+
+permissions:
+  actions: write
+  contents: write # only for delete-branch option
+  issues: write
+  pull-requests: write
 
 jobs:
-  stale:
+  issue-manager:
     runs-on: ubuntu-latest
-
     steps:
       - uses: actions/stale@v10
         with:
-          stale-issue-message: 'This issue had no activity for **14 days**. It will be closed in **1 week** unless there is some new activity. Is this issue already resolved?'
-          stale-issue-label: 'stale'
-          exempt-issue-labels: bug, potential bug, enhancement, new feature, new algo, good first issue, keep, refactoring, completed, docker
-          exempt-pr-labels: bug, potential bug, enhancement, new feature, new algo, keep, refactoring, docker
-          stale-pr-message: 'This pull request had no activity for **14 days**. It will be closed in **1 week** unless there is some new activity.'
-          stale-pr-label: 'stale'
-          days-before-stale: 14
-          days-before-close: 7
-          operations-per-run: 200
+          stale-issue-message: 'This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 10 days.'
+          close-issue-message: 'This issue was closed because it has been stalled for 10 days with no activity.'
+          days-before-issue-stale: 30
+          days-before-issue-close: 10
+          exempt-issue-labels: 'keep,awaiting-approval,work-in-progress'
+
+          stale-pr-message: 'This PR is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 10 days.'
+          close-pr-message: 'This PR was closed because it has been stalled for 10 days with no activity.'
+          days-before-pr-stale: 30
+          days-before-pr-close: 10
+          exempt-pr-labels: 'keep,awaiting-approval,work-in-progress'

.github/workflows/testing_ci.yml

@@ -44,6 +44,7 @@ jobs:
               run: |
                   which python
                   which pip
+                  pip install --upgrade setuptools pip
                   pip install torch==${{ matrix.pytorch-version }} -f https://download.pytorch.org/whl/cpu
                   python -c "import torch; print('PyTorch:', torch.__version__)"
 

README.md

@@ -113,15 +113,15 @@ embedding strategy and training approach (ORT+MIT) the same as we did in
 [the SAITS paper](https://arxiv.org/pdf/2202.08516)[^1].**
 
 The task types are abbreviated as follows:
-**`IMPU`**: Imputation;
-**`FORE`**: Forecasting;
-**`CLAS`**: Classification;
+**`IMPT`**: Imputation;
+**`FCST`**: Forecasting;
+**`CLAF`**: Classification;
 **`CLUS`**: Clustering;
 **`ANOD`**: Anomaly Detection.
 In addition to the 5 tasks, PyPOTS also provides TS2Vec[^48] for time series representation learning and vectorization.
 The paper references and links are all listed at the bottom of this file.
 
-| **Type**      | **Algo**                                                                                                                                        | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue**                                                                                         |
+| **Type**      | **Algo**                                                                                                                                        | **IMPT** | **FCST** | **CLAF** | **CLUS** | **ANOD** | **Year - Venue**                                                                                         |
 |:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:---------------------------------------------------------------------------------------------------------|
 | LLM&TSFM      | <a href="https://time-series.ai"><img src="https://pypots.com/figs/timeseriesai/logo.png" width="26px" align="center">Time-Series.AI</a>  [^36] |    ✅     |    ✅     |    ✅     |    ✅     |    ✅     | <a href="https://docs.google.com/forms/d/1Ff2ndYUFQEL3tIcwtcR8lWeopQ2vTXX6D_x8WGFKH6E">Join waitlist</a> |
 | Neural Net    | MixLinear🧑‍🔧[^52]                                                                                                                             |          |    ✅     |          |          |          | `2026 - ICLR`                                                                                            |
@@ -347,7 +347,7 @@ Here is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2
 @article{du2023pypots,
 title = {{PyPOTS: A Python Toolkit for Data Mining on Partially-Observed Time Series}},
 author = {Wenjie Du},
-journal = {KDD 2023 MiLeTS},
+journal = {SIGKDD MiLeTS Workshop},
 year = {2023},
 }
 ```

README_zh.md

@@ -105,10 +105,16 @@ PyPOTS当前支持多变量POTS数据的插补, 预测, 分类, 聚类以及异
 **为了使上述模型能够适用于POTS数据, 我们采用了与[SAITS论文](https://arxiv.org/pdf/2202.08516)[^1]
 中相同的embedding策略和训练方法(ORT+MIT)对它们进行改进**.
 
+任务类型缩写如下:
+**`IMPT`**: Imputation, 插补;
+**`FCST`**: Forecasting, 预测;
+**`CLAF`**: Classification, 分类;
+**`CLUS`**: Clustering, 聚类;
+**`ANOD`**: Anomaly Detection, 异常检测.
 除了下表中列出的5种任务(插补, 预测, 分类, 聚类和异常检测)的算法外, PyPOTS还包括了用于时序表征学习和向量化的TS2Vec[^48].
 所有算法的论文引用和链接可以在本文件的底部找到.
 
-| **Type**      | **Algo**                                                                                                                                        | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year - Venue**                                                                                         |
+| **Type**      | **Algo**                                                                                                                                        | **IMPT** | **FCST** | **CLAF** | **CLUS** | **ANOD** | **Year - Venue**                                                                                         |
 |:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:---------------------------------------------------------------------------------------------------------|
 | LLM&TSFM      | <a href="https://time-series.ai"><img src="https://pypots.com/figs/timeseriesai/logo.png" width="26px" align="center">Time-Series.AI</a>  [^36] |    ✅     |    ✅     |    ✅     |    ✅     |    ✅     | <a href="https://docs.google.com/forms/d/1Ff2ndYUFQEL3tIcwtcR8lWeopQ2vTXX6D_x8WGFKH6E">Join waitlist</a> |
 | Neural Net    | MixLinear🧑‍🔧[^52]                                                                                                                             |          |    ✅     |          |          |          | `2026 - ICLR`                                                                                            |
@@ -326,7 +332,7 @@ PyPOTS也已被纳入[PyTorch Ecosystem](https://landscape.pytorch.org/?item=mod
 @article{du2023pypots,
 title = {{PyPOTS: A Python Toolkit for Data Mining on Partially-Observed Time Series}},
 author = {Wenjie Du},
-journal = {KDD 2023 MiLeTS},
+journal = {SIGKDD MiLeTS Workshop},
 year = {2023},
 }
 ```

docs/algo_table.rst

@@ -10,9 +10,9 @@
 
    * - Type
      - Algorithm
-     - IMPU
-     - FORE
-     - CLAS
+     - IMPT
+     - FCST
+     - CLAF
      - CLUS
      - ANOD
      - Year - Venue

docs/index.rst

@@ -133,8 +133,8 @@ proposed as algorithms for POTS data in their papers, and they cannot directly a
 To make them applicable to POTS data, we specifically apply the embedding strategy and training approach (ORT+MIT)
 the same as we did in `the SAITS paper <https://arxiv.org/pdf/2202.08516)>`_ :cite:`du2023SAITS`.
 
-The task types are abbreviated as follows: **IMPU**: Imputation; **FORE**: Forecasting;
-**CLAS**: Classification; **CLUS**: Clustering; **ANOD**: Anomaly Detection.
+The task types are abbreviated as follows: **IMPT**: Imputation; **FCST**: Forecasting;
+**CLAF**: Classification; **CLUS**: Clustering; **ANOD**: Anomaly Detection.
 In addition to the 5 tasks, PyPOTS also provides TS2Vec :cite:`yue2022ts2vec` for time series representation learning and vectorization.
 The paper references are all listed at the bottom of this readme file.
 
@@ -244,7 +244,7 @@ please cite it as below and 🌟star `PyPOTS repository <https://github.com/Wenj
     @article{du2023pypots,
     title = {{PyPOTS: A Python Toolkit for Data Mining on Partially-Observed Time Series}},
     author = {Wenjie Du},
-    journal = {KDD 2023 MiLeTS},
+    journal = {SIGKDD MiLeTS Workshop},
     year = {2023},
     }
 
@@ -304,12 +304,12 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
    :caption: Getting Started
 
    install
-   examples
+   tutorials
 
 .. toctree::
    :maxdepth: 4
    :hidden:
-   :caption: Docs of PyPOTS Ecosystem
+   :caption: User Docs of PyPOTS
 
    model_api
    pypots
@@ -324,5 +324,4 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
 
    faq
    milestones
-   about_us
    references

docs/milestones.rst

@@ -16,17 +16,17 @@ please cite it as below and 🌟star `PyPOTS repository <https://github.com/Wenj
    :linenos:
 
     @article{du2023pypots,
-    title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
-    author={Wenjie Du},
-    journal={arXiv preprint arXiv:2305.18811},
-    year={2023},
+    title = {{PyPOTS: A Python Toolkit for Data Mining on Partially-Observed Time Series}},
+    author = {Wenjie Du},
+    journal = {SIGKDD MiLeTS Workshop},
+    year = {2023},
     }
 
-or
-
-   Wenjie Du. (2023).
-   PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series.
-   arXiv, abs/2305.18811. https://doi.org/10.48550/arXiv.2305.18811
+    @article{du2025pypots,
+    title = {{PyPOTS v1: A Python Toolkit for Machine Learning on Partially-Observed Time Series}},
+    author = {Wenjie Du, Yiyuan Yang, Linglong Qian, Jun Wang, and Qingsong Wen},
+    year = {2025},
+    }
 
 
 Research Projects Using PyPOTS

docs/model_api.rst

@@ -55,10 +55,10 @@ pypots.clustering.base
    :show-inheritance:
    :inherited-members:
 
-pypots.vec.base
+pypots.representation.base
 ------------------------------
 
-.. automodule:: pypots.vec.base
+.. automodule:: pypots.representation.base
    :members:
    :undoc-members:
    :show-inheritance:

docs/pypots.anomaly_detection.rst

@@ -119,7 +119,7 @@ pypots.anomaly_detection.timemixer
    :inherited-members:
 
 pypots.anomaly_detection.nonstationary_transformer
------------------------------------------------
+--------------------------------------------------
 
 .. automodule:: pypots.anomaly_detection.nonstationary_transformer
    :members:

docs/pypots.data.rst

@@ -19,20 +19,10 @@ pypots.data.saving
    :show-inheritance:
    :inherited-members:
 
-pypots.data.generating
+pypots.data.checking
 -----------------------------
 
-.. automodule:: pypots.data.generating
-   :members:
-   :undoc-members:
-   :show-inheritance:
-   :inherited-members:
-
-
-pypots.data.load\_specific\_datasets
--------------------------------------------
-
-.. automodule:: pypots.data.load_specific_datasets
+.. automodule:: pypots.data.checking
    :members:
    :undoc-members:
    :show-inheritance:

docs/pypots.imputation.rst

@@ -244,7 +244,7 @@ pypots.imputation.scinet
    :inherited-members:
 
 pypots.imputation.nonstationary_transformer
-------------------------------
+---------------------------------------------
 
 .. automodule:: pypots.imputation.nonstationary_transformer
    :members:

docs/pypots.nn.rst

@@ -177,14 +177,6 @@ pypots.nn.modules.informer
 
 
 
-pypots.nn.modules.timemoe
--------------------------------------------------
-
-.. automodule:: pypots.nn.modules.timemoe
-:members:
-
-
-
 pypots.nn.modules.dlinear
 -------------------------------------------------
 

docs/references.bib

@@ -147,17 +147,19 @@ @inproceedings{fortuin2020gpvae
 organization={PMLR}
 }
 
-@article{horn2020seft,
-title = {Set {{Functions}} for {{Time Series}}},
-author = {Horn, Max and Moor, Michael and Bock, Christian and Rieck, Bastian and Borgwardt, Karsten},
-booktitle = {Proceedings of the 37th International Conference on Machine Learning},
-pages = {4353--4363},
-year = {2020},
-editor = {Hal Daumé III and Aarti Singh},
-volume = {119},
-series = {Proceedings of Machine Learning Research},
-month = {13--18 Jul},
-publisher = {PMLR},
+@InProceedings{horn2020seft,
+  title = 	 {Set Functions for Time Series},
+  author =       {Horn, Max and Moor, Michael and Bock, Christian and Rieck, Bastian and Borgwardt, Karsten},
+  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
+  pages = 	 {4353--4363},
+  year = 	 {2020},
+  editor = 	 {III, Hal Daumé and Singh, Aarti},
+  volume = 	 {119},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {13--18 Jul},
+  publisher =    {PMLR},
+  pdf = 	 {http://proceedings.mlr.press/v119/horn20a/horn20a.pdf},
+  url = 	 {https://proceedings.mlr.press/v119/horn20a.html},
 }
 
 @article{hubert1985AdjustedRI,

docs/examples.rst → docs/tutorials.rst

@@ -1,22 +1,22 @@
-Quick-start Examples
-====================
+Quick-Start Tutorials
+================================
 
 .. image:: https://pypots.com/figs/pypots_logos/BrewPOTS/logo_FFBG.svg
    :width: 160
    :alt: BrewPOTS logo
    :align: right
    :target: https://github.com/WenjieDu/BrewPOTS
 
-We put some examples here to help our users to get started quickly.
 
 Please refer to `BrewPOTS <https://github.com/WenjieDu/BrewPOTS>`_ for detailed PyPOTS tutorials.
-You can also find a simple and quick-start tutorial notebook on Google Colab
+You can also find a simple and quick-start tutorial notebook on Google Colab by clicking the badge below
 
 .. raw:: html
 
     <a href="https://colab.research.google.com/drive/1HEFjylEy05-r47jRy0H9jiS_WhD0UWmQ" target="_blank"><img src="https://img.shields.io/badge/GoogleColab-PyPOTS_Tutorials-F9AB00?logo=googlecolab&logoColor=white"></a>
     <br clear="right">
 
+We put some examples here to help our users to get started quickly.
 
 .. code-block:: python
 

pypots/classification/seft/model.py

@@ -18,7 +18,7 @@
 
 
 class SeFT(BaseNNClassifier):
-    """The PyTorch implementation of the SeFT model :cite:`horn2020SeFT`.
+    """The PyTorch implementation of the SeFT model :cite:`horn2020seft`.
 
     Parameters
     ----------

pypots/forecasting/moderntcn/model.py

@@ -42,9 +42,11 @@ class ModernTCN(BaseNNForecaster):
 
     downsampling_ratio :
         The downsampling ratio for the downsampling mechanism.
+        Must be int, because it is also used as kernel size and stride of convolution modules in ModernTCN.
 
     ffn_ratio :
         The ratio for the feed-forward neural network in the model.
+        Must be int.
 
     num_blocks :
         The number of blocks for the model. It should be a list of integers.
@@ -135,8 +137,8 @@ def __init__(
         n_pred_features: int,
         patch_size: int,
         patch_stride: int,
-        downsampling_ratio: float,
-        ffn_ratio: float,
+        downsampling_ratio: int,
+        ffn_ratio: int,
         num_blocks: list,
         large_size: list,
         small_size: list,