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new 592705e9 update timeseries large model (#823)
592705e9 is described below
commit 592705e906ea0092e77e8545961961b440fe1ac3
Author: leto-b <[email protected]>
AuthorDate: Thu Aug 28 18:28:23 2025 +0800
update timeseries large model (#823)
* update timeseries large model
* add version
---
.../Table/AI-capability/TimeSeries-Large-Model.md | 51 ++++++++++++++++-----
.../Tree/AI-capability/TimeSeries-Large-Model.md | 51 ++++++++++++++++-----
.../AI-capability/TimeSeries-Large-Model.md | 51 ++++++++++++++++-----
.../latest/AI-capability/TimeSeries-Large-Model.md | 51 ++++++++++++++++-----
.../Table/AI-capability/TimeSeries-Large-Model.md | 53 +++++++++++++++++-----
.../Tree/AI-capability/TimeSeries-Large-Model.md | 53 +++++++++++++++++-----
.../AI-capability/TimeSeries-Large-Model.md | 53 +++++++++++++++++-----
.../latest/AI-capability/TimeSeries-Large-Model.md | 53 +++++++++++++++++-----
8 files changed, 324 insertions(+), 92 deletions(-)
diff --git a/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
b/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
index c678ad28..f53b97c1 100644
--- a/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
+++ b/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 built-in) 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(available since V2.0.5.1):
- **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(available since V2.0.5.1). 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| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
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
```
-
+## 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)
diff --git a/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
b/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
index c678ad28..f53b97c1 100644
--- a/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
+++ b/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 built-in) 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(available since V2.0.5.1):
- **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(available since V2.0.5.1). 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| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
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
```
-
+## 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)
diff --git a/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
b/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
index c678ad28..f53b97c1 100644
--- a/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
+++ b/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 built-in) 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(available since V2.0.5.1):
- **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(available since V2.0.5.1). 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| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
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
```
-
+## 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)
diff --git a/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
b/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
index c678ad28..f53b97c1 100644
--- a/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
+++ b/src/UserGuide/latest/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 built-in) 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(available since V2.0.5.1):
- **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(available since V2.0.5.1). 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| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
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
```
-
+## 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)
diff --git
a/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
b/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
index a632e1de..e51e41e5 100644
--- a/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
+++ b/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md
@@ -25,7 +25,7 @@
时序大模型是一种专为时序数据分析设计的基础模型。IoTDB
团队长期自研时序大模型，基于变换器（Transformer）结构等技术在海量时序数据上预训练，能够理解并生成多种领域的时序数据，可被应用于时序预测、异常检测、时序填补等应用场景。不同于传统时序分析技术，时序大模型具备通用特征提取能力，基于零样本分析、微调等技术服务广泛的分析任务。
-团队所研时序大模型相关技术均发表在国际机器学习顶级会议。
+团队所研时序大模型相关技术均发表在国际机器学习顶级会议（见附录）。
## 应用场景
@@ -37,7 +37,7 @@
## Timer-1 模型
-Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
+Timer<sup><a href="#appendix1" id="ref1" style="text-decoration:
none;">[1]</a></sup>
模型（非内置）不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
- **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。
- **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。
@@ -47,7 +47,7 @@ Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还
## Timer-XL 模型
-Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破：
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration:
none;">[2]</a></sup> 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破（V2.0.5.1及以后版本支持）：
- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。
- **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。
@@ -57,7 +57,7 @@ Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全
## Timer-Sundial 模型
-Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1
万亿个时间点上进行了大规模预训练，其核心特性包括：
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration:
none;">[3]</a></sup> 是一个专注于时间序列预测的生成式基础模型系列（V2.0.5.1及以后版本支持），其基础版本拥有 1.28
亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括：
- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。
- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。
@@ -92,21 +92,50 @@ Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列
1. 打开 IoTDB cli 控制台，检查 ConfigNode、DataNode、AINode 节点确保均为 Running。
-检查命令：
```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID| NodeType| Status|InternalAddress|InternalPort| Version|
BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+| 0|ConfigNode|Running| 127.0.0.1| 10710| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。
3. 检查模型是否可用
-检查命令：
-
```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
```
-
+## 附录
+
+<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.
+[↩ 返回](#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.
+[↩ 返回](#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.
+[↩ 返回](#ref3)
diff --git
a/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
b/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
index a632e1de..e51e41e5 100644
--- a/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
+++ b/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md
@@ -25,7 +25,7 @@
时序大模型是一种专为时序数据分析设计的基础模型。IoTDB
团队长期自研时序大模型，基于变换器（Transformer）结构等技术在海量时序数据上预训练，能够理解并生成多种领域的时序数据，可被应用于时序预测、异常检测、时序填补等应用场景。不同于传统时序分析技术，时序大模型具备通用特征提取能力，基于零样本分析、微调等技术服务广泛的分析任务。
-团队所研时序大模型相关技术均发表在国际机器学习顶级会议。
+团队所研时序大模型相关技术均发表在国际机器学习顶级会议（见附录）。
## 应用场景
@@ -37,7 +37,7 @@
## Timer-1 模型
-Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
+Timer<sup><a href="#appendix1" id="ref1" style="text-decoration:
none;">[1]</a></sup>
模型（非内置）不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
- **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。
- **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。
@@ -47,7 +47,7 @@ Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还
## Timer-XL 模型
-Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破：
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration:
none;">[2]</a></sup> 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破（V2.0.5.1及以后版本支持）：
- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。
- **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。
@@ -57,7 +57,7 @@ Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全
## Timer-Sundial 模型
-Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1
万亿个时间点上进行了大规模预训练，其核心特性包括：
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration:
none;">[3]</a></sup> 是一个专注于时间序列预测的生成式基础模型系列（V2.0.5.1及以后版本支持），其基础版本拥有 1.28
亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括：
- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。
- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。
@@ -92,21 +92,50 @@ Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列
1. 打开 IoTDB cli 控制台，检查 ConfigNode、DataNode、AINode 节点确保均为 Running。
-检查命令：
```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID| NodeType| Status|InternalAddress|InternalPort| Version|
BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+| 0|ConfigNode|Running| 127.0.0.1| 10710| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。
3. 检查模型是否可用
-检查命令：
-
```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
```
-
+## 附录
+
+<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.
+[↩ 返回](#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.
+[↩ 返回](#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.
+[↩ 返回](#ref3)
diff --git
a/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
b/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
index a632e1de..e51e41e5 100644
--- a/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
+++ b/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md
@@ -25,7 +25,7 @@
时序大模型是一种专为时序数据分析设计的基础模型。IoTDB
团队长期自研时序大模型，基于变换器（Transformer）结构等技术在海量时序数据上预训练，能够理解并生成多种领域的时序数据，可被应用于时序预测、异常检测、时序填补等应用场景。不同于传统时序分析技术，时序大模型具备通用特征提取能力，基于零样本分析、微调等技术服务广泛的分析任务。
-团队所研时序大模型相关技术均发表在国际机器学习顶级会议。
+团队所研时序大模型相关技术均发表在国际机器学习顶级会议（见附录）。
## 应用场景
@@ -37,7 +37,7 @@
## Timer-1 模型
-Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
+Timer<sup><a href="#appendix1" id="ref1" style="text-decoration:
none;">[1]</a></sup>
模型（非内置）不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
- **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。
- **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。
@@ -47,7 +47,7 @@ Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还
## Timer-XL 模型
-Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破：
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration:
none;">[2]</a></sup> 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破（V2.0.5.1及以后版本支持）：
- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。
- **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。
@@ -57,7 +57,7 @@ Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全
## Timer-Sundial 模型
-Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1
万亿个时间点上进行了大规模预训练，其核心特性包括：
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration:
none;">[3]</a></sup> 是一个专注于时间序列预测的生成式基础模型系列（V2.0.5.1及以后版本支持），其基础版本拥有 1.28
亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括：
- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。
- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。
@@ -92,21 +92,50 @@ Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列
1. 打开 IoTDB cli 控制台，检查 ConfigNode、DataNode、AINode 节点确保均为 Running。
-检查命令：
```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID| NodeType| Status|InternalAddress|InternalPort| Version|
BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+| 0|ConfigNode|Running| 127.0.0.1| 10710| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。
3. 检查模型是否可用
-检查命令：
-
```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
```
-
+## 附录
+
+<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.
+[↩ 返回](#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.
+[↩ 返回](#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.
+[↩ 返回](#ref3)
diff --git a/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
b/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
index a632e1de..e51e41e5 100644
--- a/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
+++ b/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md
@@ -25,7 +25,7 @@
时序大模型是一种专为时序数据分析设计的基础模型。IoTDB
团队长期自研时序大模型，基于变换器（Transformer）结构等技术在海量时序数据上预训练，能够理解并生成多种领域的时序数据，可被应用于时序预测、异常检测、时序填补等应用场景。不同于传统时序分析技术，时序大模型具备通用特征提取能力，基于零样本分析、微调等技术服务广泛的分析任务。
-团队所研时序大模型相关技术均发表在国际机器学习顶级会议。
+团队所研时序大模型相关技术均发表在国际机器学习顶级会议（见附录）。
## 应用场景
@@ -37,7 +37,7 @@
## Timer-1 模型
-Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
+Timer<sup><a href="#appendix1" id="ref1" style="text-decoration:
none;">[1]</a></sup>
模型（非内置）不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点：
- **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。
- **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。
@@ -47,7 +47,7 @@ Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还
## Timer-XL 模型
-Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破：
+Timer-XL<sup><a href="#appendix2" id="ref2" style="text-decoration:
none;">[2]</a></sup> 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破（V2.0.5.1及以后版本支持）：
- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。
- **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。
@@ -57,7 +57,7 @@ Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全
## Timer-Sundial 模型
-Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1
万亿个时间点上进行了大规模预训练，其核心特性包括：
+Timer-Sundial<sup><a href="#appendix3" id="ref3" style="text-decoration:
none;">[3]</a></sup> 是一个专注于时间序列预测的生成式基础模型系列（V2.0.5.1及以后版本支持），其基础版本拥有 1.28
亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括：
- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。
- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。
@@ -92,21 +92,50 @@ Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列
1. 打开 IoTDB cli 控制台，检查 ConfigNode、DataNode、AINode 节点确保均为 Running。
-检查命令：
```sql
-show cluster
+IoTDB> show cluster
++------+----------+-------+---------------+------------+--------------+-----------+
+|NodeID| NodeType| Status|InternalAddress|InternalPort| Version|
BuildInfo|
++------+----------+-------+---------------+------------+--------------+-----------+
+| 0|ConfigNode|Running| 127.0.0.1| 10710| 2.0.5.1|
069354f|
+| 1| DataNode|Running| 127.0.0.1| 10730| 2.0.5.1|
069354f|
+| 2| AINode|Running| 127.0.0.1| 10810|
2.0.5.1|069354f-dev|
++------+----------+-------+---------------+------------+--------------+-----------+
+Total line number = 3
+It costs 0.140s
```
-
-
2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。
3. 检查模型是否可用
-检查命令：
-
```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
```
-
+## 附录
+
+<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.
+[↩ 返回](#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.
+[↩ 返回](#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.
+[↩ 返回](#ref3)
