From 2e9ddbf93d3ba1580987aa576e9ab7c72c6e8250 Mon Sep 17 00:00:00 2001 From: leto-bbq Date: Tue, 28 Jul 2026 11:27:50 +0800 Subject: [PATCH] docs: remove Moirai2 model documentation --- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- .../AI-capability/AINode_Upgrade_apache.md | 2 -- .../TimeSeries-Large-Model_Upgrade_apache.md | 24 +++---------------- 16 files changed, 24 insertions(+), 184 deletions(-) diff --git a/src/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md b/src/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md index fc7d36253..9cb5f68b4 100644 --- a/src/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md +++ b/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
2. Covers non-stationary, multivariate, and covariate scenarios
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
2. Flexible analysis of any prediction distribution statistic
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
2. Unified multi-variable & covariate modeling (strict input requirements):
  a. Future covariate names ⊆ historical covariate names
  b. Each historical covariate length = target length
  c. Each future covariate length = prediction length
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
2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons
3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast
4. Uses instance normalization to mitigate distribution shift across series
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
2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast
3. Supports cached block decoding for efficient scaling to longer forecast horizons
4. Covariates are not currently supported | ### 4.4 Deleting Models diff --git a/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 284198ab6..760052da4 100644 --- a/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2[4]< ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 Model +## 7. Toto Model -Moirai2[5] (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[6] (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[5] (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 **[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) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ Back](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref5) diff --git a/src/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md b/src/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md index 0f614b413..be7243e64 100644 --- a/src/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md +++ b/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.
2. Multi-scenario coverage, supports non-stationary, multi-variable, and covariate prediction.
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.
2. Can flexibly analyze any statistical properties of the prediction distribution.
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.
2. Supports unified covariate modeling, but has strict input requirements:
  a. The set of names of future covariates must be a subset of the set of names of historical covariates;
  b. The length of each historical covariate must equal the length of the target variable;
  c. The length of each future covariate must equal the prediction length;
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.
2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons.
3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast.
4. Uses instance normalization to mitigate distribution shift across series.
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.
2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast.
3. Supports cached block decoding for efficient scaling to longer forecast horizons.
4. Covariates are not currently supported. | ### 4.4 Delete Models diff --git a/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 284198ab6..760052da4 100644 --- a/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2[4]< ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 Model +## 7. Toto Model -Moirai2[5] (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[6] (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[5] (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 **[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) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ Back](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref5) diff --git a/src/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md b/src/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md index fc7d36253..9cb5f68b4 100644 --- a/src/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md +++ b/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
2. Covers non-stationary, multivariate, and covariate scenarios
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
2. Flexible analysis of any prediction distribution statistic
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
2. Unified multi-variable & covariate modeling (strict input requirements):
  a. Future covariate names ⊆ historical covariate names
  b. Each historical covariate length = target length
  c. Each future covariate length = prediction length
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
2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons
3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast
4. Uses instance normalization to mitigate distribution shift across series
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
2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast
3. Supports cached block decoding for efficient scaling to longer forecast horizons
4. Covariates are not currently supported | ### 4.4 Deleting Models diff --git a/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 284198ab6..760052da4 100644 --- a/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2[4]< ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 Model +## 7. Toto Model -Moirai2[5] (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[6] (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[5] (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 **[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) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ Back](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref5) diff --git a/src/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md b/src/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md index 0f614b413..be7243e64 100644 --- a/src/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md +++ b/src/UserGuide/latest/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.
2. Multi-scenario coverage, supports non-stationary, multi-variable, and covariate prediction.
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.
2. Can flexibly analyze any statistical properties of the prediction distribution.
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.
2. Supports unified covariate modeling, but has strict input requirements:
  a. The set of names of future covariates must be a subset of the set of names of historical covariates;
  b. The length of each historical covariate must equal the length of the target variable;
  c. The length of each future covariate must equal the prediction length;
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.
2. Predicts multiple patches per decoding step to reduce autoregressive overhead for long horizons.
3. Outputs nine quantiles (0.1–0.9) and uses the p50 median as the point forecast.
4. Uses instance normalization to mitigate distribution shift across series.
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.
2. Outputs fixed quantiles from 0.1 to 0.9 and uses the p50 median as the point forecast.
3. Supports cached block decoding for efficient scaling to longer forecast horizons.
4. Covariates are not currently supported. | ### 4.4 Delete Models diff --git a/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 284198ab6..760052da4 100644 --- a/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2[4]< ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 Model +## 7. Toto Model -Moirai2[5] (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[6] (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[5] (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 **[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) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ Back](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ Back](#ref5) diff --git a/src/zh/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md b/src/zh/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md index 915ef8691..9ed5e3446 100644 --- a/src/zh/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md +++ b/src/zh/UserGuide/Master/Table/AI-capability/AINode_Upgrade_apache.md @@ -447,7 +447,6 @@ 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| +---------------------+--------------+--------------+-------------+ ``` @@ -472,7 +471,6 @@ IoTDB> show models | **Timer-XL** | 支持超长上下文的时序大模型，通过大规模工业数据预训练增强泛化能力。 | 需利用极长历史数据的复杂工业预测，如能源、航空航天、交通等领域。 | 1. 超长上下文支持，可处理数万时间点输入。
2. 多场景覆盖，支持非平稳、多变量及协变量预测。
3. 基于万亿级高质量工业时序数据预训练。 | | **Timer-Sundial** | 采用“Transformer + TimeFlow”架构的生成式基础模型，专注于概率预测。 | 需要量化不确定性的零样本预测场景，如金融、供应链、新能源发电预测。 | 1. 强大的零样本泛化能力，支持点预测与概率预测
2. 可灵活分析预测分布的任意统计特性。
3. 创新生成架构，实现高效的非确定性样本生成。 | | **Chronos-2** | 基于离散词元化范式的通用时序基础模型，将预测转化为语言建模任务。 | 快速零样本单变量预测，以及可借助协变量（如促销、天气）提升效果的场景。 | 1. 强大的零样本概率预测能力。
2. 支持协变量统一建模，但对输入有严格要求：
  a. 未来协变量的名称组成的集合必须是历史协变量的名称组成的集合的子集；
  b. 每个历史协变量的长度必须等于目标变量的长度；
  c. 每个未来协变量的长度必须等于预测长度；
3. 采用高效的编码器式结构，兼顾性能与推理速度。 | -| **Moirai 2.0** | 采用轻量级 Decoder-only Patch Transformer，通过单一 Patch 尺寸、多 Token 预测和多分位数输出实现高效的单变量预测。 | 适用于对模型体量和推理效率要求较高的零样本单变量预测，如工业监测、能源负荷及设备指标预测。 | 1. 模型参数量约 11.4M。
2. 每个解码步可预测多个 Patch，降低长预测范围的自回归开销。
3. 输出 9 个分位数（0.1～0.9），使用 p50 中位数作为点预测。
4. 使用实例归一化缓解序列分布漂移。
5. 不支持多变量和协变量。 | | **Toto 2.0** | 采用 Decoder-only Patch Transformer，交替使用因果时间注意力与变量注意力，对时间维和变量维进行联合建模。 | 面向可观测性指标等多变量时间序列的零样本预测，如 CPU、内存、网络流量等指标的联合预测。 | 1. 支持单变量和多变量目标预测。
2. 输出 0.1～0.9 的固定分位数，使用 p50 中位数作为点预测。
3. 支持缓存式块解码，可高效扩展较长预测范围。
4. 当前不支持协变量。 | ### 4.4 删除模型 diff --git a/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 5fe5c3771..537acb5b9 100644 --- a/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2 [4] ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 模型 +## 7. Toto 模型 -Moirai2[5]（Moirai 2.0）是由 Salesforce AI Research 推出的通用时间序列基础模型（V2.0.10 及以后版本支持）。当前集成的是 Moirai 2.0 R-small 版本，模型参数量约为 11.4M。与采用掩码编码器架构的 Moirai 1.0 不同，Moirai 2.0 使用因果 Decoder-only Patch Transformer，并通过单一 Patch 尺寸、多 Token 预测和多分位数输出，在较小模型规模下实现高效的单变量预测。其核心特性包括： - -- **轻量化模型架构**：采用 Decoder-only Patch Transformer，并结合 RMSNorm、旋转位置编码和 SiLU-GLU 前馈网络，在较小参数规模下兼顾预测能力和推理效率。 -- **多 Token 预测**：每个解码步骤可同时预测多个 Patch，减少长预测范围下所需的自回归解码次数。 -- **概率性预测能力**：模型输出 0.1～0.9 共 9 个分位数，AINode 使用 p50 中位数作为点预测结果。 -- **Patch 解码**：在进入注意力模块前，将时间序列按照固定大小的 Patch 进行分组，以提高时序特征提取和解码效率。 -- **实例归一化**：在模型输入前对每条时间序列进行标准化，并在输出后执行反归一化，以缓解不同序列间的分布漂移。 -- **输入范围**：模型聚焦于单变量预测，不支持多变量目标及协变量输入。 - -![](/img/LargeModel-moirai2.png) - -> 注意：Moirai 2.0 R-small 模型权重采用 CC BY-NC 4.0 许可证，仅限研究用途。 - -## 8. Toto 模型 - -Toto[6]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： +Toto[5]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： - **单变量与多变量预测**：支持单个目标变量以及多个相关目标变量的联合预测，适用于 CPU、内存、网络流量等可观测性指标的分析场景。 - **概率性预测能力**：模型输出 0.1～0.9 的固定分位数，可刻画预测结果的不确定性；AINode 使用 p50 中位数作为点预测结果。 @@ -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 **[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.** [↩ 返回](#ref4) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ 返回](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref5) diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md index 255eaff80..b580ca818 100644 --- a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md +++ b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_Upgrade_apache.md @@ -423,7 +423,6 @@ 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| +---------------------+--------------+--------------+-------------+ ``` @@ -448,7 +447,6 @@ IoTDB> show models | **Timer-XL** | 支持超长上下文的时序大模型，通过大规模工业数据预训练增强泛化能力。 | 需利用极长历史数据的复杂工业预测，如能源、航空航天、交通等领域。 | 1. 超长上下文支持，可处理数万时间点输入。
2. 多场景覆盖，支持非平稳、多变量及协变量预测。
3. 基于万亿级高质量工业时序数据预训练。 | | **Timer-Sundial** | 采用“Transformer + TimeFlow”架构的生成式基础模型，专注于概率预测。 | 需要量化不确定性的零样本预测场景，如金融、供应链、新能源发电预测。 | 1. 强大的零样本泛化能力，支持点预测与概率预测
2. 可灵活分析预测分布的任意统计特性。
3. 创新生成架构，实现高效的非确定性样本生成。 | | **Chronos-2** | 基于离散词元化范式的通用时序基础模型，将预测转化为语言建模任务。 | 快速零样本单变量预测，以及可借助协变量（如促销、天气）提升效果的场景。 | 1. 强大的零样本概率预测能力。
2. 支持协变量统一建模，但对输入有严格要求：
  a. 未来协变量的名称组成的集合必须是历史协变量的名称组成的集合的子集；
  b. 每个历史协变量的长度必须等于目标变量的长度；
  c. 每个未来协变量的长度必须等于预测长度；
3. 采用高效的编码器式结构，兼顾性能与推理速度。 | -| **Moirai 2.0** | 采用轻量级 Decoder-only Patch Transformer，通过单一 Patch 尺寸、多 Token 预测和多分位数输出实现高效的单变量预测。 | 适用于对模型体量和推理效率要求较高的零样本单变量预测，如工业监测、能源负荷及设备指标预测。 | 1. 模型参数量约 11.4M。
2. 每个解码步可预测多个 Patch，降低长预测范围的自回归开销。
3. 输出 9 个分位数（0.1～0.9），使用 p50 中位数作为点预测。
4. 使用实例归一化缓解序列分布漂移。
5. 不支持多变量和协变量。 | | **Toto 2.0** | 采用 Decoder-only Patch Transformer，交替使用因果时间注意力与变量注意力，对时间维和变量维进行联合建模。 | 面向可观测性指标等多变量时间序列的零样本预测，如 CPU、内存、网络流量等指标的联合预测。 | 1. 支持单变量和多变量目标预测。
2. 输出 0.1～0.9 的固定分位数，使用 p50 中位数作为点预测。
3. 支持缓存式块解码，可高效扩展较长预测范围。
4. 当前不支持协变量。 | ### 4.4 删除模型 diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 5fe5c3771..537acb5b9 100644 --- a/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2 [4] ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 模型 +## 7. Toto 模型 -Moirai2[5]（Moirai 2.0）是由 Salesforce AI Research 推出的通用时间序列基础模型（V2.0.10 及以后版本支持）。当前集成的是 Moirai 2.0 R-small 版本，模型参数量约为 11.4M。与采用掩码编码器架构的 Moirai 1.0 不同，Moirai 2.0 使用因果 Decoder-only Patch Transformer，并通过单一 Patch 尺寸、多 Token 预测和多分位数输出，在较小模型规模下实现高效的单变量预测。其核心特性包括： - -- **轻量化模型架构**：采用 Decoder-only Patch Transformer，并结合 RMSNorm、旋转位置编码和 SiLU-GLU 前馈网络，在较小参数规模下兼顾预测能力和推理效率。 -- **多 Token 预测**：每个解码步骤可同时预测多个 Patch，减少长预测范围下所需的自回归解码次数。 -- **概率性预测能力**：模型输出 0.1～0.9 共 9 个分位数，AINode 使用 p50 中位数作为点预测结果。 -- **Patch 解码**：在进入注意力模块前，将时间序列按照固定大小的 Patch 进行分组，以提高时序特征提取和解码效率。 -- **实例归一化**：在模型输入前对每条时间序列进行标准化，并在输出后执行反归一化，以缓解不同序列间的分布漂移。 -- **输入范围**：模型聚焦于单变量预测，不支持多变量目标及协变量输入。 - -![](/img/LargeModel-moirai2.png) - -> 注意：Moirai 2.0 R-small 模型权重采用 CC BY-NC 4.0 许可证，仅限研究用途。 - -## 8. Toto 模型 - -Toto[6]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： +Toto[5]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： - **单变量与多变量预测**：支持单个目标变量以及多个相关目标变量的联合预测，适用于 CPU、内存、网络流量等可观测性指标的分析场景。 - **概率性预测能力**：模型输出 0.1～0.9 的固定分位数，可刻画预测结果的不确定性；AINode 使用 p50 中位数作为点预测结果。 @@ -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 **[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.** [↩ 返回](#ref4) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ 返回](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref5) diff --git a/src/zh/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md b/src/zh/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md index 915ef8691..9ed5e3446 100644 --- a/src/zh/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md +++ b/src/zh/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md @@ -447,7 +447,6 @@ 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| +---------------------+--------------+--------------+-------------+ ``` @@ -472,7 +471,6 @@ IoTDB> show models | **Timer-XL** | 支持超长上下文的时序大模型，通过大规模工业数据预训练增强泛化能力。 | 需利用极长历史数据的复杂工业预测，如能源、航空航天、交通等领域。 | 1. 超长上下文支持，可处理数万时间点输入。
2. 多场景覆盖，支持非平稳、多变量及协变量预测。
3. 基于万亿级高质量工业时序数据预训练。 | | **Timer-Sundial** | 采用“Transformer + TimeFlow”架构的生成式基础模型，专注于概率预测。 | 需要量化不确定性的零样本预测场景，如金融、供应链、新能源发电预测。 | 1. 强大的零样本泛化能力，支持点预测与概率预测
2. 可灵活分析预测分布的任意统计特性。
3. 创新生成架构，实现高效的非确定性样本生成。 | | **Chronos-2** | 基于离散词元化范式的通用时序基础模型，将预测转化为语言建模任务。 | 快速零样本单变量预测，以及可借助协变量（如促销、天气）提升效果的场景。 | 1. 强大的零样本概率预测能力。
2. 支持协变量统一建模，但对输入有严格要求：
  a. 未来协变量的名称组成的集合必须是历史协变量的名称组成的集合的子集；
  b. 每个历史协变量的长度必须等于目标变量的长度；
  c. 每个未来协变量的长度必须等于预测长度；
3. 采用高效的编码器式结构，兼顾性能与推理速度。 | -| **Moirai 2.0** | 采用轻量级 Decoder-only Patch Transformer，通过单一 Patch 尺寸、多 Token 预测和多分位数输出实现高效的单变量预测。 | 适用于对模型体量和推理效率要求较高的零样本单变量预测，如工业监测、能源负荷及设备指标预测。 | 1. 模型参数量约 11.4M。
2. 每个解码步可预测多个 Patch，降低长预测范围的自回归开销。
3. 输出 9 个分位数（0.1～0.9），使用 p50 中位数作为点预测。
4. 使用实例归一化缓解序列分布漂移。
5. 不支持多变量和协变量。 | | **Toto 2.0** | 采用 Decoder-only Patch Transformer，交替使用因果时间注意力与变量注意力，对时间维和变量维进行联合建模。 | 面向可观测性指标等多变量时间序列的零样本预测，如 CPU、内存、网络流量等指标的联合预测。 | 1. 支持单变量和多变量目标预测。
2. 输出 0.1～0.9 的固定分位数，使用 p50 中位数作为点预测。
3. 支持缓存式块解码，可高效扩展较长预测范围。
4. 当前不支持协变量。 | ### 4.4 删除模型 diff --git a/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 0a75fce5b..45ee65df6 100644 --- a/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2 [4] ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 模型 +## 7. Toto 模型 -Moirai2[5]（Moirai 2.0）是由 Salesforce AI Research 推出的通用时间序列基础模型（V2.0.10 及以后版本支持）。当前集成的是 Moirai 2.0 R-small 版本，模型参数量约为 11.4M。与采用掩码编码器架构的 Moirai 1.0 不同，Moirai 2.0 使用因果 Decoder-only Patch Transformer，并通过单一 Patch 尺寸、多 Token 预测和多分位数输出，在较小模型规模下实现高效的单变量预测。其核心特性包括： - -- **轻量化模型架构**：采用 Decoder-only Patch Transformer，并结合 RMSNorm、旋转位置编码和 SiLU-GLU 前馈网络，在较小参数规模下兼顾预测能力和推理效率。 -- **多 Token 预测**：每个解码步骤可同时预测多个 Patch，减少长预测范围下所需的自回归解码次数。 -- **概率性预测能力**：模型输出 0.1～0.9 共 9 个分位数，AINode 使用 p50 中位数作为点预测结果。 -- **Patch 解码**：在进入注意力模块前，将时间序列按照固定大小的 Patch 进行分组，以提高时序特征提取和解码效率。 -- **实例归一化**：在模型输入前对每条时间序列进行标准化，并在输出后执行反归一化，以缓解不同序列间的分布漂移。 -- **输入范围**：模型聚焦于单变量预测，不支持多变量目标及协变量输入。 - -![](/img/LargeModel-moirai2.png) - -> 注意：Moirai 2.0 R-small 模型权重采用 CC BY-NC 4.0 许可证，仅限研究用途。 - -## 8. Toto 模型 - -Toto[6]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： +Toto[5]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： - **单变量与多变量预测**：支持单个目标变量以及多个相关目标变量的联合预测，适用于 CPU、内存、网络流量等可观测性指标的分析场景。 - **概率性预测能力**：模型输出 0.1～0.9 的固定分位数，可刻画预测结果的不确定性；AINode 使用 p50 中位数作为点预测结果。 @@ -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 **[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.** [↩ 返回](#ref4) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ 返回](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref5) diff --git a/src/zh/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md b/src/zh/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md index 255eaff80..b580ca818 100644 --- a/src/zh/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md +++ b/src/zh/UserGuide/latest/AI-capability/AINode_Upgrade_apache.md @@ -423,7 +423,6 @@ 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| +---------------------+--------------+--------------+-------------+ ``` @@ -448,7 +447,6 @@ IoTDB> show models | **Timer-XL** | 支持超长上下文的时序大模型，通过大规模工业数据预训练增强泛化能力。 | 需利用极长历史数据的复杂工业预测，如能源、航空航天、交通等领域。 | 1. 超长上下文支持，可处理数万时间点输入。
2. 多场景覆盖，支持非平稳、多变量及协变量预测。
3. 基于万亿级高质量工业时序数据预训练。 | | **Timer-Sundial** | 采用“Transformer + TimeFlow”架构的生成式基础模型，专注于概率预测。 | 需要量化不确定性的零样本预测场景，如金融、供应链、新能源发电预测。 | 1. 强大的零样本泛化能力，支持点预测与概率预测
2. 可灵活分析预测分布的任意统计特性。
3. 创新生成架构，实现高效的非确定性样本生成。 | | **Chronos-2** | 基于离散词元化范式的通用时序基础模型，将预测转化为语言建模任务。 | 快速零样本单变量预测，以及可借助协变量（如促销、天气）提升效果的场景。 | 1. 强大的零样本概率预测能力。
2. 支持协变量统一建模，但对输入有严格要求：
  a. 未来协变量的名称组成的集合必须是历史协变量的名称组成的集合的子集；
  b. 每个历史协变量的长度必须等于目标变量的长度；
  c. 每个未来协变量的长度必须等于预测长度；
3. 采用高效的编码器式结构，兼顾性能与推理速度。 | -| **Moirai 2.0** | 采用轻量级 Decoder-only Patch Transformer，通过单一 Patch 尺寸、多 Token 预测和多分位数输出实现高效的单变量预测。 | 适用于对模型体量和推理效率要求较高的零样本单变量预测，如工业监测、能源负荷及设备指标预测。 | 1. 模型参数量约 11.4M。
2. 每个解码步可预测多个 Patch，降低长预测范围的自回归开销。
3. 输出 9 个分位数（0.1～0.9），使用 p50 中位数作为点预测。
4. 使用实例归一化缓解序列分布漂移。
5. 不支持多变量和协变量。 | | **Toto 2.0** | 采用 Decoder-only Patch Transformer，交替使用因果时间注意力与变量注意力，对时间维和变量维进行联合建模。 | 面向可观测性指标等多变量时间序列的零样本预测，如 CPU、内存、网络流量等指标的联合预测。 | 1. 支持单变量和多变量目标预测。
2. 输出 0.1～0.9 的固定分位数，使用 p50 中位数作为点预测。
3. 支持缓存式块解码，可高效扩展较长预测范围。
4. 当前不支持协变量。 | ### 4.4 删除模型 diff --git a/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md b/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md index 0a75fce5b..45ee65df6 100644 --- a/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md +++ b/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model_Upgrade_apache.md @@ -78,24 +78,9 @@ Chronos-2 [4] ![](/img/timeseries-large-model-chronos2.png) -## 7. Moirai2 模型 +## 7. Toto 模型 -Moirai2[5]（Moirai 2.0）是由 Salesforce AI Research 推出的通用时间序列基础模型（V2.0.10 及以后版本支持）。当前集成的是 Moirai 2.0 R-small 版本，模型参数量约为 11.4M。与采用掩码编码器架构的 Moirai 1.0 不同，Moirai 2.0 使用因果 Decoder-only Patch Transformer，并通过单一 Patch 尺寸、多 Token 预测和多分位数输出，在较小模型规模下实现高效的单变量预测。其核心特性包括： - -- **轻量化模型架构**：采用 Decoder-only Patch Transformer，并结合 RMSNorm、旋转位置编码和 SiLU-GLU 前馈网络，在较小参数规模下兼顾预测能力和推理效率。 -- **多 Token 预测**：每个解码步骤可同时预测多个 Patch，减少长预测范围下所需的自回归解码次数。 -- **概率性预测能力**：模型输出 0.1～0.9 共 9 个分位数，AINode 使用 p50 中位数作为点预测结果。 -- **Patch 解码**：在进入注意力模块前，将时间序列按照固定大小的 Patch 进行分组，以提高时序特征提取和解码效率。 -- **实例归一化**：在模型输入前对每条时间序列进行标准化，并在输出后执行反归一化，以缓解不同序列间的分布漂移。 -- **输入范围**：模型聚焦于单变量预测，不支持多变量目标及协变量输入。 - -![](/img/LargeModel-moirai2.png) - -> 注意：Moirai 2.0 R-small 模型权重采用 CC BY-NC 4.0 许可证，仅限研究用途。 - -## 8. Toto 模型 - -Toto[6]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： +Toto[5]（Toto 2.0）是由 Datadog 推出的新一代时间序列基础模型（V2.0.10 及以后版本支持），主要面向可观测性场景中的时间序列预测。当前集成的模型采用 2.5B 参数版本，基于 Decoder-only Patch Transformer 架构，交替使用因果时间注意力与变量注意力，对时间维度和变量维度进行联合建模。其核心特性包括： - **单变量与多变量预测**：支持单个目标变量以及多个相关目标变量的联合预测，适用于 CPU、内存、网络流量等可观测性指标的分析场景。 - **概率性预测能力**：模型输出 0.1～0.9 的固定分位数，可刻画预测结果的不确定性；AINode 使用 p50 中位数作为点预测结果。 @@ -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 **[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.** [↩ 返回](#ref4) -[5] Moirai 2.0: When Less Is More for Time Series Forecasting, Salesforce AI Research, arXiv:2511.11698. [↩ 返回](#ref5) - -[6] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref6) +[5] Toto 2.0: Time Series Forecasting Enters the Scaling Era, Datadog, arXiv:2605.20119. [↩ 返回](#ref5)