docs: remove Moirai2 model documentation

src/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md

@@ -436,7 +436,6 @@ IoTDB> show models
 |           sundialx_4|       sundial|    fine_tuned|     training|
 |           sundialx_5|       sundial|    fine_tuned|       failed|
 |             chronos2|            t5|       builtin|     inactive|
-|              moirai2|        moirai|       builtin|     inactive|
 |                 toto|          toto|       builtin|     inactive|
 +---------------------+--------------+--------------+-------------+
 ```
@@ -461,7 +460,6 @@ IoTDB> show models
 | **Timer-XL**    | Long-context time series large model pretrained on massive industrial data  | Complex industrial forecasting requiring ultra-long history (energy, aerospace, transport) | 1. Supports input of tens of thousands of time points<br>2. Covers non-stationary, multivariate, and covariate scenarios<br>3. Pretrained on trillion-scale high-quality industrial IoT data |
 | **Timer-Sundial** | Generative foundation model with "Transformer + TimeFlow" architecture     | Zero-shot forecasting requiring uncertainty quantification (finance, supply chain, renewable energy) | 1. Strong zero-shot generalization; supports point & probabilistic forecasting<br>2. Flexible analysis of any prediction distribution statistic<br>3. Innovative flow-matching architecture for efficient non-deterministic sample generation |
 | **Chronos-2**   | Universal time series foundation model based on discrete tokenization       | Rapid zero-shot univariate forecasting; scenarios enhanced by covariates (promotions, weather) | 1. Powerful zero-shot probabilistic forecasting<br>2. Unified multi-variable & covariate modeling (strict input requirements):<br>&nbsp;&nbsp;a. Future covariate names ⊆ historical covariate names<br>&nbsp;&nbsp;b. Each historical covariate length = target length<br>&nbsp;&nbsp;c. Each future covariate length = prediction length<br>3. Efficient encoder-only structure balancing performance and speed |
-| **Moirai 2.0** | Lightweight decoder-only Patch Transformer with a single patch size, multi-token prediction, and multi-quantile outputs | Zero-shot univariate forecasting where model size and inference efficiency are important, such as industrial monitoring, energy load, and equipment metrics | 1. Approximately 11.4M parameters<br>2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons<br>3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast<br>4. Uses instance normalization to mitigate distribution shift across series<br>5. Does not support multivariate targets or covariates |
 | **Toto 2.0** | Decoder-only Patch Transformer alternating causal temporal attention and variable attention to jointly model temporal and variable dimensions | Zero-shot multivariate forecasting for observability metrics, including joint forecasting of CPU, memory, and network traffic | 1. Supports univariate and multivariate target forecasting<br>2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast<br>3. Supports cached block decoding for efficient scaling to longer forecast horizons<br>4. Covariates are not currently supported |
 
 ### 4.4 Deleting Models

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

@@ -78,24 +78,9 @@ Chronos-2<sup><a href="#appendix4" id="ref4" style="text-decoration: none;">[4]<
 
 ![](/img/timeseries-large-model-chronos2.png)
 
-## 7. Moirai2 Model
+## 7. Toto Model
 
-Moirai2<sup><a href="#appendix5" id="ref5" style="text-decoration: none;">[5]</a></sup> (Moirai 2.0) is a general-purpose time series foundation model developed by Salesforce AI Research (supported in V2.0.10 and later). AINode currently integrates the Moirai 2.0 R-small variant, which has approximately 11.4M parameters. Unlike Moirai 1.0, which uses a masked encoder architecture, Moirai 2.0 uses a causal decoder-only Patch Transformer. With a single patch size, multi-token prediction, and multi-quantile outputs, it provides efficient univariate forecasting with a compact model. Its core features include:
-
-- **Lightweight Model Architecture**: Uses a decoder-only Patch Transformer with RMSNorm, rotary positional embeddings, and SiLU-GLU feed-forward networks to balance forecasting capability and inference efficiency at a small parameter scale.
-- **Multi-Token Prediction**: Predicts multiple patches at each decoding step, reducing the number of autoregressive decoding steps required for long forecast horizons.
-- **Probabilistic Forecasting**: Outputs nine quantiles from 0.1 to 0.9. AINode uses the p50 median as the point forecast.
-- **Patch Decoding**: Groups the time series into fixed-size patches before the attention module to improve temporal feature extraction and decoding efficiency.
-- **Instance Normalization**: Standardizes each time series before model input and applies denormalization after output to mitigate distribution shifts across series.
-- **Input Scope**: Focuses on univariate forecasting and does not support multivariate targets or covariates.
-
-![](/img/LargeModel-moirai2.png)
-
-> Note: The Moirai 2.0 R-small model weights are licensed under CC BY-NC 4.0 and are restricted to research use.
-
-## 8. Toto Model
-
-Toto<sup><a href="#appendix6" id="ref6" style="text-decoration: none;">[6]</a></sup> (Toto 2.0) is a next-generation time series foundation model developed by Datadog (supported in V2.0.10 and later), primarily for forecasting in observability scenarios. AINode currently integrates the 2.5B-parameter variant. It is based on a decoder-only Patch Transformer architecture that alternates causal temporal attention and variable attention to jointly model the temporal and variable dimensions. Its core features include:
+Toto<sup><a href="#appendix5" id="ref5" style="text-decoration: none;">[5]</a></sup> (Toto 2.0) is a next-generation time series foundation model developed by Datadog (supported in V2.0.10 and later), primarily for forecasting in observability scenarios. AINode currently integrates the 2.5B-parameter variant. It is based on a decoder-only Patch Transformer architecture that alternates causal temporal attention and variable attention to jointly model the temporal and variable dimensions. Its core features include:
 
 - **Univariate and Multivariate Forecasting**: Supports both individual target variables and joint forecasting of multiple related target variables, making it suitable for observability metrics such as CPU, memory, and network traffic.
 - **Probabilistic Forecasting**: Outputs fixed quantiles from 0.1 to 0.9 to represent forecasting uncertainty. AINode uses the p50 median as the point forecast.
@@ -170,7 +155,6 @@ IoTDB> show models
 |             timer_xl|    timer| builtin|  active|
 |              sundial|  sundial| builtin|  active|
 |             chronos2|       t5| builtin|  active|
-|              moirai2|   moirai| builtin|  active|
 |                 toto|     toto| builtin|  active|
 +---------------------+---------+--------+--------+
 ```
@@ -185,6 +169,4 @@ IoTDB> show models
 
 <a id="appendix4"></a>**[4]** Chronos-2: From Univariate to Universal Forecasting, Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider, **arXiv:2510.15821**. [↩ Back](#ref4)
 
-<a id="appendix5"></a>[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ Back](#ref5)
-
-<a id="appendix6"></a>[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref6)
+<a id="appendix5"></a>[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref5)

src/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md

@@ -420,7 +420,6 @@ IoTDB> show models
 |           sundialx_4|       sundial|    fine_tuned|     training|
 |           sundialx_5|       sundial|    fine_tuned|       failed|
 |             chronos2|            t5|       builtin|     inactive|
-|              moirai2|        moirai|       builtin|     inactive|
 |                 toto|          toto|       builtin|     inactive|
 +---------------------+--------------+--------------+-------------+
 ```
@@ -445,7 +444,6 @@ Built-in time series large model introduction:
 | **Timer-XL**  | Time series large model supporting ultra-long context, enhancing generalization capability through large-scale industrial data pre-training.   | Complex industrial prediction requiring extremely long historical data, such as energy, aerospace, and transportation.       | 1. Ultra-long context support, can handle tens of thousands of time points as input. <br>2. Multi-scenario coverage, supports non-stationary, multi-variable, and covariate prediction. <br>3. Pre-trained on trillions of high-quality industrial time series data.                                                                                                                                      |
 | **Timer-Sundial** | A generative foundational model based on "Transformer + TimeFlow" architecture, focusing on probabilistic prediction. | Zero-shot prediction scenarios requiring quantification of uncertainty, such as finance, supply chain, and new energy power generation.     | 1. Strong zero-shot generalization capability, supports point prediction and probabilistic prediction. <br>2. Can flexibly analyze any statistical properties of the prediction distribution. <br>3. Innovative generative architecture, achieving efficient non-deterministic sample generation.                                                                                                                                      |
 | **Chronos-2** | A general time series foundational model based on discrete tokenization paradigm, converting prediction into language modeling tasks.     | Rapid zero-shot univariate prediction, and scenarios that can leverage covariates (e.g., promotions, weather) to improve results. | 1. Strong zero-shot probabilistic prediction capability. <br>2. Supports unified covariate modeling, but has strict input requirements:<br>&nbsp;&nbsp;a. The set of names of future covariates must be a subset of the set of names of historical covariates;<br>&nbsp;&nbsp;b. The length of each historical covariate must equal the length of the target variable;<br>&nbsp;&nbsp;c. The length of each future covariate must equal the prediction length;<br>3. Uses an efficient encoder-style structure, balancing performance and inference speed. |
-| **Moirai 2.0** | Uses a lightweight decoder-only Patch Transformer with a single patch size, multi-token prediction, and multi-quantile outputs for efficient univariate forecasting. | Zero-shot univariate forecasting where model size and inference efficiency are important, such as industrial monitoring, energy load, and equipment metric forecasting. | 1. Approximately 11.4M parameters.<br>2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons.<br>3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast.<br>4. Uses instance normalization to mitigate distribution shift across series.<br>5. Does not support multivariate targets or covariates. |
 | **Toto 2.0** | Uses a decoder-only Patch Transformer that alternates causal temporal attention and variable attention to jointly model the temporal and variable dimensions. | Zero-shot forecasting for multivariate time series such as observability metrics, including joint forecasting of CPU, memory, and network traffic. | 1. Supports univariate and multivariate target forecasting.<br>2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast.<br>3. Supports cached block decoding for efficient scaling to longer forecast horizons.<br>4. Covariates are not currently supported. |
 
 ### 4.4 Delete Models

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

@@ -78,24 +78,9 @@ Chronos-2<sup><a href="#appendix4" id="ref4" style="text-decoration: none;">[4]<
 
 ![](/img/timeseries-large-model-chronos2.png)
 
-## 7. Moirai2 Model
+## 7. Toto Model
 
-Moirai2<sup><a href="#appendix5" id="ref5" style="text-decoration: none;">[5]</a></sup> (Moirai 2.0) is a general-purpose time series foundation model developed by Salesforce AI Research (supported in V2.0.10 and later). AINode currently integrates the Moirai 2.0 R-small variant, which has approximately 11.4M parameters. Unlike Moirai 1.0, which uses a masked encoder architecture, Moirai 2.0 uses a causal decoder-only Patch Transformer. With a single patch size, multi-token prediction, and multi-quantile outputs, it provides efficient univariate forecasting with a compact model. Its core features include:
-
-- **Lightweight Model Architecture**: Uses a decoder-only Patch Transformer with RMSNorm, rotary positional embeddings, and SiLU-GLU feed-forward networks to balance forecasting capability and inference efficiency at a small parameter scale.
-- **Multi-Token Prediction**: Predicts multiple patches at each decoding step, reducing the number of autoregressive decoding steps required for long forecast horizons.
-- **Probabilistic Forecasting**: Outputs nine quantiles from 0.1 to 0.9. AINode uses the p50 median as the point forecast.
-- **Patch Decoding**: Groups the time series into fixed-size patches before the attention module to improve temporal feature extraction and decoding efficiency.
-- **Instance Normalization**: Standardizes each time series before model input and applies denormalization after output to mitigate distribution shifts across series.
-- **Input Scope**: Focuses on univariate forecasting and does not support multivariate targets or covariates.
-
-![](/img/LargeModel-moirai2.png)
-
-> Note: The Moirai 2.0 R-small model weights are licensed under CC BY-NC 4.0 and are restricted to research use.
-
-## 8. Toto Model
-
-Toto<sup><a href="#appendix6" id="ref6" style="text-decoration: none;">[6]</a></sup> (Toto 2.0) is a next-generation time series foundation model developed by Datadog (supported in V2.0.10 and later), primarily for forecasting in observability scenarios. AINode currently integrates the 2.5B-parameter variant. It is based on a decoder-only Patch Transformer architecture that alternates causal temporal attention and variable attention to jointly model the temporal and variable dimensions. Its core features include:
+Toto<sup><a href="#appendix5" id="ref5" style="text-decoration: none;">[5]</a></sup> (Toto 2.0) is a next-generation time series foundation model developed by Datadog (supported in V2.0.10 and later), primarily for forecasting in observability scenarios. AINode currently integrates the 2.5B-parameter variant. It is based on a decoder-only Patch Transformer architecture that alternates causal temporal attention and variable attention to jointly model the temporal and variable dimensions. Its core features include:
 
 - **Univariate and Multivariate Forecasting**: Supports both individual target variables and joint forecasting of multiple related target variables, making it suitable for observability metrics such as CPU, memory, and network traffic.
 - **Probabilistic Forecasting**: Outputs fixed quantiles from 0.1 to 0.9 to represent forecasting uncertainty. AINode uses the p50 median as the point forecast.
@@ -170,7 +155,6 @@ IoTDB> show models
 |             timer_xl|    timer| builtin|  active|
 |              sundial|  sundial| builtin|  active|
 |             chronos2|       t5| builtin|  active|
-|              moirai2|   moirai| builtin|  active|
 |                 toto|     toto| builtin|  active|
 +---------------------+---------+--------+--------+
 ```
@@ -185,6 +169,4 @@ IoTDB> show models
 
 <a id="appendix4"></a>**[4]** Chronos-2: From Univariate to Universal Forecasting, Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider, **arXiv:2510.15821**. [↩ Back](#ref4)
 
-<a id="appendix5"></a>[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ Back](#ref5)
-
-<a id="appendix6"></a>[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref6)
+<a id="appendix5"></a>[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref5)

src/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md

@@ -436,7 +436,6 @@ IoTDB> show models
 |           sundialx_4|       sundial|    fine_tuned|     training|
 |           sundialx_5|       sundial|    fine_tuned|       failed|
 |             chronos2|            t5|       builtin|     inactive|
-|              moirai2|        moirai|       builtin|     inactive|
 |                 toto|          toto|       builtin|     inactive|
 +---------------------+--------------+--------------+-------------+
 ```
@@ -461,7 +460,6 @@ IoTDB> show models
 | **Timer-XL**    | Long-context time series large model pretrained on massive industrial data  | Complex industrial forecasting requiring ultra-long history (energy, aerospace, transport) | 1. Supports input of tens of thousands of time points<br>2. Covers non-stationary, multivariate, and covariate scenarios<br>3. Pretrained on trillion-scale high-quality industrial IoT data |
 | **Timer-Sundial** | Generative foundation model with "Transformer + TimeFlow" architecture     | Zero-shot forecasting requiring uncertainty quantification (finance, supply chain, renewable energy) | 1. Strong zero-shot generalization; supports point & probabilistic forecasting<br>2. Flexible analysis of any prediction distribution statistic<br>3. Innovative flow-matching architecture for efficient non-deterministic sample generation |
 | **Chronos-2**   | Universal time series foundation model based on discrete tokenization       | Rapid zero-shot univariate forecasting; scenarios enhanced by covariates (promotions, weather) | 1. Powerful zero-shot probabilistic forecasting<br>2. Unified multi-variable & covariate modeling (strict input requirements):<br>&nbsp;&nbsp;a. Future covariate names ⊆ historical covariate names<br>&nbsp;&nbsp;b. Each historical covariate length = target length<br>&nbsp;&nbsp;c. Each future covariate length = prediction length<br>3. Efficient encoder-only structure balancing performance and speed |
-| **Moirai 2.0** | Lightweight decoder-only Patch Transformer with a single patch size, multi-token prediction, and multi-quantile outputs | Zero-shot univariate forecasting where model size and inference efficiency are important, such as industrial monitoring, energy load, and equipment metrics | 1. Approximately 11.4M parameters<br>2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons<br>3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast<br>4. Uses instance normalization to mitigate distribution shift across series<br>5. Does not support multivariate targets or covariates |
 | **Toto 2.0** | Decoder-only Patch Transformer alternating causal temporal attention and variable attention to jointly model temporal and variable dimensions | Zero-shot multivariate forecasting for observability metrics, including joint forecasting of CPU, memory, and network traffic | 1. Supports univariate and multivariate target forecasting<br>2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast<br>3. Supports cached block decoding for efficient scaling to longer forecast horizons<br>4. Covariates are not currently supported |
 
 ### 4.4 Deleting Models