update readme and paddlets version for 1.1.0 release. (PaddlePaddle#339)

README_cn.md

@@ -20,26 +20,30 @@ PaddleTS 是一个易用的深度时序建模的Python库，它基于飞桨深
 
 * 设计统一数据结构，实现对多样化时序数据的表达，支持单目标与多目标变量，支持多类型协变量
 * 封装基础模型功能，如数据加载、回调设置、损失函数、训练过程控制等公共方法，帮助开发者在新模型开发过程中专注网络结构本身
-* 内置业界领先的深度学习模型，包括NBEATS、NHiTS、LSTNet、TCN、Transformer, DeepAR（概率预测）、Informer等时序预测模型，以及TS2Vec等时序表征模型
+* 内置业界领先的深度学习模型，包括NBEATS、NHiTS、LSTNet、TCN、Transformer、DeepAR、Informer等时序预测模型，
+  TS2Vec、CoST等时序表征模型，以及
+  Autoencoder、VAE、AnomalyTransformer等时序异常检测模型
 * 内置多样化的数据转换算子，支持数据处理与转换，包括缺失值填充、异常值处理、归一化、时间相关的协变量提取等
 * 内置经典的数据分析算子，帮助开发者便捷实现数据探索，包括数据统计量信息及数据摘要等功能
 * 自动模型调优AutoTS，支持多类型HPO(Hyper Parameter Optimization)算法，在多个模型和数据集上展现显著调优效果 
-* 第三方机器学习模型及数据转换模块自动集成，支持包括sklearn、pyod等第三方库的时序应用
+* 第三方机器学习模型及数据转换模块自动集成，支持包括sklearn、[pyod](https://github.com/yzhao062/pyod)等第三方库的时序应用
 * 支持在GPU设备上运行基于PaddlePaddle的时序模型
+* 时序模型集成学习能力
 
 最新更新：
-* 发布时序表征模型 Contrastive Learning of Disentangled Seasonal-trend Representations(CoST)
-* 支持时序异常检测，发布包括AE (Autoencoder)、VAE (Variational Autoencoder)、AnomalyTransformer深度学习模型
-* 支持第三方时序异常检测库[pyod](https://github.com/yzhao062/pyod)自动集成
-* 新增时序模型集成学习能力。目前发布版本支持StackingEnsembleForecaster和WeightingEnsembleForecaster等2种时序预测集成学习方法
-* RNN模型现在支持使用分类特征和静态协变量
-* 新增基于表征模型的时序预测功能，支持使用时序表征模型解决时序预测任务
-* 支持基于多时序数据集的组合训练
+* 新增时序分类能力
+* 全新发布6个深度时序模型。
+  USAD(UnSupervised Anomaly Detection)与MTAD_GAT(Multivariate Time-series Anomaly Detection via Graph Attention Network)异常检测模型，
+  CNN与Inception Time时序分类模型，
+  SCINet(Sample Convolution and Interaction Network)与TFT(Temporal Fusion Transformer)时序预测模型
+* 新发布[Paddle Inference](https://www.paddlepaddle.org.cn/paddle/paddleinference)支持，已适配时序预测与时序异常检测
+* 新增模型可解释性能力。包括模型无关的可解释性与模型相关的可解释性
+* 新增支持基于表征的聚类与分类
+
+您也可以参考[发布说明](https://github.com/PaddlePaddle/PaddleTS/wiki/Release-Notes)获取更详尽的更新列表。
 
 未来，更多的高级特性会进一步发布，包括但不限于：
-* 更多时序异常检测模型
-* 更多时序表征模型
-* 更多概率预测模型
+* 更多时序模型
 * 场景化Pipeline，支持端到端真实场景解决方案
 
 
@@ -56,10 +60,12 @@ PaddleTS 是一个易用的深度时序建模的Python库，它基于飞桨深
 | [**paddlets.models.forecasting**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/models/overview.html)          | 时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序预测模型   |
 | [**paddlets.models.representation**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/models/representation.html) | 时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序表征模型   |
 | [**paddlets.models.anomaly**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/models/anomaly.html)               | 时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序异常检测模型 |
+| [**paddlets.models.classify**](https://paddlets.readthedocs.io/zh_CN/latest/source/api/paddlets.models.classify.html)        | 时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序分类模型   |
 | [**paddlets.pipeline**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/pipeline/overview.html)                  | 建模任务流模块，支持特征工程、模型训练、模型评估的任务流实现         |
 | [**paddlets.metrics**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/metrics/overview.html)                    | 效果评估模块，提供多维度模型评估能力                     |
 | [**paddlets.analysis**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/analysis/overview.html)                  | 数据分析模块，提供高效的时序特色数据分析能力                 |
 | [**paddlets.ensemble**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/ensemble/overview.html)                  | 时序集成学习模块，基于模型集成提供时序预测能力                |
+| [**paddlets.xai**](https://paddlets.readthedocs.io/zh_CN/latest/source/api/paddlets.xai.html)                                | 时序模型可解释性模块                             |
 | [**paddlets.utils**](https://paddlets.readthedocs.io/zh_CN/latest/source/modules/backtest/overview.html)                     | 工具集模块，提供回测等基础功能                        |
 
 

README_en.md

@@ -32,28 +32,32 @@ PaddleTS is an easy-to-use Python library for deep time series modeling,
     loss computation, training loop control, etc.) and allows developers to focus on 
     the implementation of network architectures when developing new models
 * A set of state-of-the-art deep learning models containing
-    NBEATS, NHiTS, LSTNet, TCN, Transformer, DeepAR(Probabilistic), Informer, etc. for forecasting, TS2Vec for representation
+    NBEATS, NHiTS, LSTNet, TCN, Transformer, DeepAR, Informer, etc. for forecasting, 
+    TS2Vec, CoST, etc. for representation,
+    AutoEncoder, VAE, AnomalyTransformer, etc. for anomaly detection
 * A set of transformation operators for data preprocessing (e.g. missing values/outliers handling, 
     one-hot encoding, normalization, and automatic date/time-related covariates generation, etc.)
 * A set of analysis operators for quick data exploration (e.g. basic statistics and summary)
 * Automatic time series modeling module (AutoTS) which supports mainstream Hyper Parameter Optimization algorithms and shows significant improvement on multiple models and datasets
-* Third-party (e.g. scikit-learn) ML models & data transformations integration
+* Third-party (e.g. scikit-learn, [pyod](https://github.com/yzhao062/pyod)) ML models & data transformations integration
+* Time series model ensemble
 
 Recently updated:
 
-* Released a new time series representation model, i.e. Contrastive Learning of Disentangled Seasonal-trend Representations(CoST)
-* Time series anomaly detection model supported, with three deep models released, including AE(AutoEncoder), VAE(Variational AutoEncoder), and AnomalyTransformer
-* Third-party [pyod](https://github.com/yzhao062/pyod) ML models integration supported
-* Support time series model ensemble with two types of ensemble forecaster, StackingEnsembleForecaster and WeightingEnsembleForecaster proposed
-* RNN time series forecasting model supports categorical features and static covariates
-* New representation forecaster to support representation models to solve time series forecasting task
-* Support joint training of multiple time series datasets
+* PaddleTS now supports time series classification
+* PaddleTS releases 6 new time series models. 
+  USAD(UnSupervised Anomaly Detection) and MTAD-GAT(Multivariate Time-series Anomaly Detection via Graph Attention Network) for anomaly detection,
+  CNN and Inception Time for time series classification, 
+  SCINet(Sample Convolution and Interaction Network) and TFT(Temporal Fusion Transformer) for forecasting
+* [Paddle Inference](https://www.paddlepaddle.org.cn/paddle/paddleinference) is now available for PaddleTS time series forecasting and anomaly detection
+* PaddleTS now supports both model-agnostic and model-specific explanation
+* PaddleTS now supports representation-based time series cluster and classification
+
+Please also see [release notes](https://github.com/PaddlePaddle/PaddleTS/wiki/Release-Notes) to get exhaustive update lists.
 
 In the future, more advanced features will be coming, including:
 
-* More time series anomaly detection models
-* More time series representation learning models
-* More probabilistic forecasting models
+* More time series models
 * Scenario-specific pipelines which aim to provide an end-to-end solution for solving real-world business problems
 * And more
 
@@ -71,10 +75,12 @@ Specifically, PaddleTS consists of the following modules:
 | [**paddlets.models.forecasting**](https://paddlets.readthedocs.io/en/latest/source/modules/models/overview.html)          | PaddlePaddle-based deep neural network models for time series forecasting.                    |
 | [**paddlets.models.representation**](https://paddlets.readthedocs.io/en/latest/source/modules/models/representation.html) | PaddlePaddle-based deep neural network models for time series representation.                 |
 | [**paddlets.models.anomaly**](https://paddlets.readthedocs.io/en/latest/source/modules/models/anomaly.html)               | PaddlePaddle-based deep neural network models for time series anomaly detection.              |
+| [**paddlets.models.classify**](https://paddlets.readthedocs.io/en/latest/source/api/paddlets.models.classify.html)        | PaddlePaddle-based deep neural network models for time series classification.                 |
 | [**paddlets.pipeline**](https://paddlets.readthedocs.io/en/latest/source/modules/pipeline/overview.html)                  | Pipeline for building time series analysis and modeling workflows.                            |
 | [**paddlets.metrics**](https://paddlets.readthedocs.io/en/latest/source/modules/metrics/overview.html)                    | Metrics for measuring the performance of a model.                                             |
 | [**paddlets.analysis**](https://paddlets.readthedocs.io/en/latest/source/modules/analysis/overview.html)                  | Quick data exploration and advanced data analysis.                                            |
 | [**paddlets.ensemble**](https://paddlets.readthedocs.io/en/latest/source/modules/ensemble/overview.html)                  | Time series ensemble methods.                                                                 |
+| [**paddlets.xai**](https://paddlets.readthedocs.io/en/latest/source/api/paddlets.xai.html)                                | Model-agnostic and model-specific explanation for time series modeling.                       |
 | [**paddlets.utils**](https://paddlets.readthedocs.io/en/latest/source/modules/backtest/overview.html)                     | Utility functions.                                                                            |
 
 

paddlets/__init__.py

@@ -4,4 +4,4 @@
 from paddlets.datasets import TimeSeries, TSDataset
 from paddlets.pipeline import Pipeline
 
-__version__="1.0.2"
+__version__="1.1.0"