Merge branch 'release/3.0-beta1' into develop

docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection_en.md

@@ -7,7 +7,6 @@ Time series anomaly detection focuses on identifying abnormal points or periods
 
 ## II. Supported Model List
 
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 | Model Name | Precision | Recall | F1-Score | Model Size (M) | Description |
 |-|-|-|-|-|-|
 | AutoEncoder_ad_ad | 0.9898 | 0.9396 | 0.9641 | 72.8K | AutoEncoder_ad_ad is a simple, efficient, and easy-to-use time series anomaly detection model |
@@ -18,7 +17,6 @@ Time series anomaly detection focuses on identifying abnormal points or periods
 
 **Note: The above accuracy metrics are measured on the PSM dataset with a time series length of 100.**
 
-
 ## III. Quick Integration
 > ❗ Before quick integration, please install the PaddleX wheel package. For details, refer to the [PaddleX Local Installation Guide](../../../installation/installation_en.md)
 
@@ -236,6 +234,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * PaddleX abstracts the concepts of dynamic graph weights and static graph weights from you. During model training, both dynamic and static graph weights are produced, and static graph weights are used by default for model inference.
 * After model training, all outputs are saved in the specified output directory (default is `./output/`), typically including:
 
+
 * `train_result.json`: Training result record file, including whether the training task completed successfully, produced weight metrics, and related file paths.
 * `train.log`: Training log file, recording model metric changes, loss changes, etc.
 * `config.yaml`: Training configuration file, recording the hyperparameters used for this training session.

docs/module_usage/tutorials/time_series_modules/time_series_classification_en.md

@@ -7,14 +7,12 @@ Time series classification involves identifying and categorizing different patte
 
 ## II. Supported Model List
 
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 |Model Name|Acc(%)|Model Size (M)|Description|
 |-|-|-|-|
 |TimesNet_cls|87.5|792K|TimesNet is an adaptive and high-accuracy time series classification model through multi-period analysis|
 
 **Note: The evaluation set for the above accuracy metrics is UWaveGestureLibrary.**
 
-
 ## III. Quick Integration
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to [PaddleX Local Installation Guide](../../../installation/installation_en.md)
 

docs/module_usage/tutorials/time_series_modules/time_series_forecasting_en.md

@@ -7,7 +7,6 @@ Time series forecasting aims to predict the possible values or states at a futur
 
 ## II. Supported Model List
 
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 |Model Name| mse | mae |Model Size (M)| Introduce |
 |-|-|-|-|-|
 |DLinear|0.382|0.394|76k|Simple structure, high efficiency and easy-to-use time series prediction model|
@@ -20,7 +19,6 @@ Time series forecasting aims to predict the possible values or states at a futur
 **Note: The above accuracy metrics are measured on the [ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar) test dataset, with an input sequence length of 96, and a prediction sequence length of 96 for all models except TiDE, which has a prediction sequence length of 720.**
 
 
-
 ## III. Quick Integration
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation_en.md)
 

docs/support_list/models_list_en.md

@@ -343,6 +343,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 
 **Note: The evaluation set for the above accuracy metrics is the ****PaddleX self-built Layout Detection Dataset****, containing 10,000 images.**
 
+
 ## [Time Series Forecasting Module](../module_usage/tutorials/time_series_modules/time_series_forecasting_en.md)
 |Model Name|mse|mae|Model Size|YAML File|
 |-|-|-|-|-|