update timeseries large model

src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md

@@ -37,7 +37,7 @@ The team's related technologies of time series large models have been published
 
 ## Timer Model
 
-The Timer model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following:
+The Timer<sup><a href="#appendix1" id="ref1" style="text-decoration: none;">[1]</a></sup> model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following:
 
 - **Generalization**: The model can be fine-tuned using a small number of samples to achieve leading predictive performance in the industry.
 - **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios.
@@ -47,7 +47,7 @@ The Timer model not only demonstrates excellent few-shot generalization and mult
 
 ## Timer-XL Model
 
-Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions:
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration: none;">[2]</a></sup> is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions:
 
 - **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem.
 - **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs.
@@ -57,7 +57,7 @@ Timer-XL is an upgraded version of Timer that further extends the network struct
 
 ## Timer-Sundial Model
 
-Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include:
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration: none;">[3]</a></sup> is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include:
 
 - **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously.
 - **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model.
@@ -92,22 +92,51 @@ Utilizing time series large models to accurately identify outliers that deviate
 
 1. Open the IoTDB CLI console and verify that the ConfigNode, DataNode, and AINode statuses are all ​Running.
 
-Check command:
-
 ```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID|  NodeType| Status|InternalAddress|InternalPort|       Version|  BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+|     0|ConfigNode|Running|      127.0.0.1|       10710|2.0.4-SNAPSHOT|    069354f|
+|     1|  DataNode|Running|      127.0.0.1|       10730|2.0.4-SNAPSHOT|    069354f|
+|     2|    AINode|Running|      127.0.0.1|       10810|2.0.4-SNAPSHOT|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
 ```
 
-![](/img/ainode-timer-1.png)
-
 2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled.
 
 3. Verify model registration success
 
-Check command:
 
 ```sql
-show models
+IoTDB:etth> show models
++---------------------+--------------------+--------+------+
+|              ModelId|           ModelType|Category| State|
++---------------------+--------------------+--------+------+
+|                arima|               Arima|BUILT-IN|ACTIVE|
+|          holtwinters|         HoltWinters|BUILT-IN|ACTIVE|
+|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE|
+|     naive_forecaster|     NaiveForecaster|BUILT-IN|ACTIVE|
+|       stl_forecaster|       StlForecaster|BUILT-IN|ACTIVE|
+|         gaussian_hmm|         GaussianHmm|BUILT-IN|ACTIVE|
+|              gmm_hmm|              GmmHmm|BUILT-IN|ACTIVE|
+|                stray|               Stray|BUILT-IN|ACTIVE|
+|              sundial|       Timer-Sundial|BUILT-IN|ACTIVE|
+|             timer_xl|            Timer-XL|BUILT-IN|ACTIVE|
++---------------------+--------------------+--------+------+
+Total line number = 10
+It costs 0.004s
 ```
 
-![](/img/LargeModel06.png)
+## Appendix
+
+<a id="appendix1"></a>[1] Timer- Generative Pre-trained Transformers Are Large Time Series Models, Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long.
+[↩ ](#ref1)
+
+<a id="appendix2"></a>[2] TIMER-XL- LONG-CONTEXT TRANSFORMERS FOR UNIFIED TIME SERIES FORECASTING ,Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long.
+[↩ ](#ref2)
+
+<a id="appendix3"></a>[3] Sundial- A Family of Highly Capable Time Series Foundation Models, Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, Mingsheng Long, ICML 2025 spotlight.
+[↩ ](#ref3)

src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md

@@ -37,7 +37,7 @@ The team's related technologies of time series large models have been published
 
 ## Timer Model
 
-The Timer model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following:
+The Timer<sup><a href="#appendix1" id="ref1" style="text-decoration: none;">[1]</a></sup> model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following:
 
 - **Generalization**: The model can be fine-tuned using a small number of samples to achieve leading predictive performance in the industry.
 - **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios.
@@ -47,7 +47,7 @@ The Timer model not only demonstrates excellent few-shot generalization and mult
 
 ## Timer-XL Model
 
-Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions:
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration: none;">[2]</a></sup> is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions:
 
 - **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem.
 - **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs.
@@ -57,7 +57,7 @@ Timer-XL is an upgraded version of Timer that further extends the network struct
 
 ## Timer-Sundial Model
 
-Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include:
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration: none;">[3]</a></sup> is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include:
 
 - **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously.
 - **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model.
@@ -92,22 +92,51 @@ Utilizing time series large models to accurately identify outliers that deviate
 
 1. Open the IoTDB CLI console and verify that the ConfigNode, DataNode, and AINode statuses are all ​Running.
 
-Check command:
-
 ```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID|  NodeType| Status|InternalAddress|InternalPort|       Version|  BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+|     0|ConfigNode|Running|      127.0.0.1|       10710|2.0.4-SNAPSHOT|    069354f|
+|     1|  DataNode|Running|      127.0.0.1|       10730|2.0.4-SNAPSHOT|    069354f|
+|     2|    AINode|Running|      127.0.0.1|       10810|2.0.4-SNAPSHOT|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
 ```
 
-![](/img/ainode-timer-1.png)
-
 2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled.
 
 3. Verify model registration success
 
-Check command:
 
 ```sql
-show models
+IoTDB:etth> show models
++---------------------+--------------------+--------+------+
+|              ModelId|           ModelType|Category| State|
++---------------------+--------------------+--------+------+
+|                arima|               Arima|BUILT-IN|ACTIVE|
+|          holtwinters|         HoltWinters|BUILT-IN|ACTIVE|
+|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE|
+|     naive_forecaster|     NaiveForecaster|BUILT-IN|ACTIVE|
+|       stl_forecaster|       StlForecaster|BUILT-IN|ACTIVE|
+|         gaussian_hmm|         GaussianHmm|BUILT-IN|ACTIVE|
+|              gmm_hmm|              GmmHmm|BUILT-IN|ACTIVE|
+|                stray|               Stray|BUILT-IN|ACTIVE|
+|              sundial|       Timer-Sundial|BUILT-IN|ACTIVE|
+|             timer_xl|            Timer-XL|BUILT-IN|ACTIVE|
++---------------------+--------------------+--------+------+
+Total line number = 10
+It costs 0.004s
 ```
 
-![](/img/LargeModel06.png)
+## Appendix
+
+<a id="appendix1"></a>[1] Timer- Generative Pre-trained Transformers Are Large Time Series Models, Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long.
+[↩ ](#ref1)
+
+<a id="appendix2"></a>[2] TIMER-XL- LONG-CONTEXT TRANSFORMERS FOR UNIFIED TIME SERIES FORECASTING ,Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long.
+[↩ ](#ref2)
+
+<a id="appendix3"></a>[3] Sundial- A Family of Highly Capable Time Series Foundation Models, Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, Mingsheng Long, ICML 2025 spotlight.
+[↩ ](#ref3)

src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md

@@ -37,7 +37,7 @@ The team's related technologies of time series large models have been published
 
 ## Timer Model
 
-The Timer model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following:
+The Timer<sup><a href="#appendix1" id="ref1" style="text-decoration: none;">[1]</a></sup> model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following:
 
 - **Generalization**: The model can be fine-tuned using a small number of samples to achieve leading predictive performance in the industry.
 - **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios.
@@ -47,7 +47,7 @@ The Timer model not only demonstrates excellent few-shot generalization and mult
 
 ## Timer-XL Model
 
-Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions:
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration: none;">[2]</a></sup> is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions:
 
 - **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem.
 - **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs.
@@ -57,7 +57,7 @@ Timer-XL is an upgraded version of Timer that further extends the network struct
 
 ## Timer-Sundial Model
 
-Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include:
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration: none;">[3]</a></sup> is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include:
 
 - **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously.
 - **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model.
@@ -92,22 +92,51 @@ Utilizing time series large models to accurately identify outliers that deviate
 
 1. Open the IoTDB CLI console and verify that the ConfigNode, DataNode, and AINode statuses are all ​Running.
 
-Check command:
-
 ```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID|  NodeType| Status|InternalAddress|InternalPort|       Version|  BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+|     0|ConfigNode|Running|      127.0.0.1|       10710|2.0.4-SNAPSHOT|    069354f|
+|     1|  DataNode|Running|      127.0.0.1|       10730|2.0.4-SNAPSHOT|    069354f|
+|     2|    AINode|Running|      127.0.0.1|       10810|2.0.4-SNAPSHOT|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
 ```
 
-![](/img/ainode-timer-1.png)
-
 2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled.
 
 3. Verify model registration success
 
-Check command:
 
 ```sql
-show models
+IoTDB:etth> show models
++---------------------+--------------------+--------+------+
+|              ModelId|           ModelType|Category| State|
++---------------------+--------------------+--------+------+
+|                arima|               Arima|BUILT-IN|ACTIVE|
+|          holtwinters|         HoltWinters|BUILT-IN|ACTIVE|
+|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE|
+|     naive_forecaster|     NaiveForecaster|BUILT-IN|ACTIVE|
+|       stl_forecaster|       StlForecaster|BUILT-IN|ACTIVE|
+|         gaussian_hmm|         GaussianHmm|BUILT-IN|ACTIVE|
+|              gmm_hmm|              GmmHmm|BUILT-IN|ACTIVE|
+|                stray|               Stray|BUILT-IN|ACTIVE|
+|              sundial|       Timer-Sundial|BUILT-IN|ACTIVE|
+|             timer_xl|            Timer-XL|BUILT-IN|ACTIVE|
++---------------------+--------------------+--------+------+
+Total line number = 10
+It costs 0.004s
 ```
 
-![](/img/LargeModel06.png)
+## Appendix
+
+<a id="appendix1"></a>[1] Timer- Generative Pre-trained Transformers Are Large Time Series Models, Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long.
+[↩ ](#ref1)
+
+<a id="appendix2"></a>[2] TIMER-XL- LONG-CONTEXT TRANSFORMERS FOR UNIFIED TIME SERIES FORECASTING ,Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long.
+[↩ ](#ref2)
+
+<a id="appendix3"></a>[3] Sundial- A Family of Highly Capable Time Series Foundation Models, Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, Mingsheng Long, ICML 2025 spotlight.
+[↩ ](#ref3)