Are Self-Attentions Effective for Time Series Forecasting?

Dongbin Kim, Jinseong Park, Jaewook Lee, Hoki Kim·May 27, 2024

Summary

This paper investigates the effectiveness of self-attention in time series forecasting, particularly focusing on the Cross-Attention-only Time Series Transformer (CATS), which removes self-attention and employs cross-attention mechanisms. CATS enhances long-term forecasting accuracy by introducing future horizon-dependent parameters, enhanced parameter sharing, and query-adaptive masking. Experiments across various datasets demonstrate that CATS outperforms existing models, such as PatchTST and TimeMixer, in terms of lower mean squared error, fewer parameters, and reduced memory usage. The model's simplicity and improved efficiency make it a strong alternative, especially for long input sequences, while also providing insights into prediction processes through attention maps. The study challenges the dominance of complex Transformer-based approaches and suggests that more streamlined architectures can achieve state-of-the-art performance in time series forecasting.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the effectiveness of self-attentions in time series forecasting by proposing a new cross-attention-based architecture that eliminates self-attentions . This study introduces a novel forecasting architecture called Cross-Attention-only Time Series transformer (CATS) that focuses on cross-attentions and future horizon-dependent parameters as queries, aiming to enhance parameter sharing and improve long-term forecasting performance . While the issue of self-attentions in time series forecasting has been previously noted , this paper contributes by proposing a specific architecture that eliminates self-attentions and emphasizes cross-attentions, offering a new approach to the problem .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that cross-attention-based architecture in Transformer models is more effective for time series forecasting compared to using self-attentions . The study explores the advantages of Transformer models by emphasizing the significance of cross-attention mechanisms in handling the complexities of long-term forecasting tasks . The research provides insights into the reevaluation of self-attentions in time series forecasting and encourages further investigation into the efficacy and efficiency of attention mechanisms across various time series analysis tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel approach for time series forecasting by introducing a new cross-attention-based architecture that removes self-attentions . This model aims to establish a strong baseline for forecasting tasks and offers insights into the complexities of long-term forecasting problems . The proposed architecture consists of three key components:

  • Cross-Attention with Future as Query
  • Parameter Sharing across Horizons
  • Query-Adaptive Masking .

By leveraging advanced architectural designs of time series transformers, the model prioritizes cross-attention without self-attention, which linear models cannot utilize effectively . The study emphasizes the importance of maintaining the periodic properties of time series data while utilizing the structural advantages of the transformer architecture .

Furthermore, the paper highlights the efficiency of the proposed model through parameter sharing and demonstrates significant efficiency through parameter sharing, where the input length doubles, showcasing the model's capability for parameter sharing and efficiency . The research also addresses the challenge of channel independence between variables in time series data and suggests future research to address cross-variate dependency with reduced computation complexity based on the proposed architecture . The proposed model in the paper introduces a novel approach for time series forecasting by prioritizing cross-attention without self-attention, leveraging advanced architectural designs of time series transformers . This model not only preserves the temporal information of time series data similar to linear models but also utilizes the structural advantages of the transformer architecture, maintaining the periodic properties of time series data . The architecture consists of three key components: Cross-Attention with Future as Query, Parameter Sharing across Horizons, and Query-Adaptive Masking, which collectively enhance forecasting performance .

Compared to previous methods, the proposed model, named Cross-Attention-only Time Series transformer (CATS), simplifies the original Transformer architecture by eliminating all self-attentions and focusing on the potential of cross-attentions . By establishing future horizon-dependent parameters as queries and treating past time series data as key and value pairs, the model enhances parameter sharing and improves long-term forecasting performance . CATS demonstrates superior forecasting performance with lower mean squared error, even for longer input sequences, and requires fewer parameters than existing models, showcasing its efficiency and effectiveness .

Additionally, the paper highlights the effectiveness of cross-attention in the proposed structure by comparing it with self-attention layers. The study confirms that the cross-attention mechanism outperforms self-attention layers in most cases, emphasizing the advantages of prioritizing cross-attention for time series forecasting tasks . This comparison underscores the model's capability to achieve higher performance with a more efficient structure by focusing on cross-attentions instead of self-attentions .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of time series forecasting using Transformer models. Noteworthy researchers in this area include:

  • Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam .
  • Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski .
  • Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger .
  • Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan .
  • Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang .
  • Tian Zhou, Ziqing Ma, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, et al. .
  • Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, and JUN ZHOU .
  • Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi .
  • Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long .
  • Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long .
  • Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe Lin, and Alan Yuille .
  • Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu .
  • Haokui Zhang, Wenze Hu, and Xiaoyu Wang .
  • Yunhao Zhang and Junchi Yan .

The key to the solution mentioned in the paper involves developing a new cross-attention-based architecture for time series forecasting by removing self-attentions. This model establishes a strong baseline for forecasting tasks and offers insights into the complexities of long-term forecasting problems. The proposed architecture prioritizes cross-attention without self-attention, leveraging advanced designs of time series transformers, such as cross-attention with future as query, parameter sharing across horizons, and query-adaptive masking .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific settings and parameters to evaluate the effectiveness of self-attentions for time series forecasting . The experimental settings included different datasets such as Weather, Traffic, Electricity, and ETT datasets . These datasets were chosen for their periodic characteristics and real-world relevance in forecasting tasks . The experiments utilized fixed hyperparameters, including a random seed of 2021 for reproducibility, input sequence length L = 96, and forecasting horizon T values of [96, 192, 336, 720] . The model configurations involved using the GeGLU activation function, learnable positional embedding parameters, and three cross-attention layers with specific embedding size and number of attention heads . Additionally, the experiments compared the performance of models with different attention mechanisms, such as cross-attention and self-attention layers, to assess their impact on forecasting accuracy . The results of these experiments provided insights into the effectiveness of cross-attention mechanisms in improving prediction accuracy by leveraging periodic information within the time series data .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on self-attentions for time series forecasting includes seven datasets: Weather, Traffic, Electricity, and ETT (ETTh1, ETTh2, ETTm1, ETTm2) . The datasets capture various periodic characteristics and scenarios relevant for long-term time series forecasting tasks. However, the information about whether the code is open source is not explicitly mentioned in the provided context. For details regarding the availability of the code, it is recommended to refer to the original source of the study or contact the authors directly for clarification.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified in the context of time series forecasting using self-attentions and cross-attentions . The paper extensively evaluates various models and their performance metrics across different datasets, such as Weather, Electricity, Traffic, and ETT datasets, demonstrating the effectiveness of the proposed models in handling time series forecasting tasks.

The experiments include detailed comparisons of models with unified hyperparameter settings, showcasing the performance of different forecasting models in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) . Additionally, the paper explores the impact of attention mechanisms, specifically cross-attention and self-attention layers, on the forecasting accuracy of the models . The results consistently highlight the superiority of cross-attention mechanisms over self-attention layers in improving forecasting performance across various datasets and input sequence lengths .

Furthermore, the paper provides in-depth analyses of the attention mechanisms' ability to capture periodic patterns in time series data, enhancing the interpretability and predictive performance of the models . The visualizations of cross-attention score maps and forecasting results illustrate how the models leverage periodic information to make accurate predictions . These visualizations help in understanding how the models detect shocks and periodic components within the time series data, contributing to the validation of the scientific hypotheses.

Overall, the comprehensive experimental results, performance comparisons, and visualizations presented in the paper offer substantial evidence to support the scientific hypotheses related to the effectiveness of self-attentions and cross-attentions for time series forecasting tasks. The analyses conducted in the paper contribute to advancing the understanding of attention mechanisms in time series forecasting and provide a solid foundation for future research in this domain.


What are the contributions of this paper?

The paper makes significant contributions to the field of time series forecasting by:

  • Introducing a new cross-attention-based architecture that removes self-attentions, establishing a strong baseline for forecasting tasks and offering insights into long-term forecasting complexities .
  • Providing a reevaluation of self-attentions in time series forecasting and highlighting the importance of assessing efficacy and efficiency across different time series analysis tasks .
  • Addressing the limitation of assuming channel independence between variables by proposing methods to handle cross-variate dependency with reduced computation complexity based on the developed architecture .

What work can be continued in depth?

Further research in the field of time series forecasting can be extended by critically assessing the efficacy and efficiency of Transformer architecture across various time series analysis tasks . Additionally, future studies can focus on addressing cross-variate dependency with reduced computation complexity based on proposed architectures to better handle the highly correlated nature of real-world time series data .


Introduction
Background
Evolution of time series forecasting models
Importance of self-attention in sequence analysis
Objective
To evaluate CATS' performance
Challenge the need for complex Transformers
Investigate efficiency in long-term forecasting
Method
Data Collection
Selection of diverse time series datasets
Data preprocessing techniques (if applicable)
Data Preprocessing
Handling missing values and normalization
Resampling and feature engineering (if needed)
CATS Architecture
Description of Cross-Attention-only Transformer
Future horizon-dependent parameters
Enhanced parameter sharing
Query-adaptive masking mechanism
Model Evaluation
Performance metrics (MSE, parameters, memory usage)
Comparison with PatchTST and TimeMixer
Experiment design and replication
Efficiency Analysis
Speed and resource consumption tests
Scalability with input sequence length
Attention Map Interpretation
Visualization of attention patterns
Insights into prediction processes
Results
CATS' superior performance in forecasting accuracy
Comparative analysis with state-of-the-art models
Memory and computational efficiency benefits
Discussion
Implications for the dominance of complex Transformers
Limitations and potential improvements
Future research directions
Conclusion
Summary of findings
CATS as a strong alternative for time series forecasting
The value of streamlined architectures in the field
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What advantages does CATS offer in terms of accuracy, model size, and memory usage compared to PatchTST and TimeMixer?
What does the paper focus on in terms of time series forecasting?
How does the Cross-Attention-only Time Series Transformer (CATS) differ from traditional self-attention mechanisms?
What are the key factors that contribute to CATS' improved performance and efficiency in long-term forecasting?

Are Self-Attentions Effective for Time Series Forecasting?

Dongbin Kim, Jinseong Park, Jaewook Lee, Hoki Kim·May 27, 2024

Summary

This paper investigates the effectiveness of self-attention in time series forecasting, particularly focusing on the Cross-Attention-only Time Series Transformer (CATS), which removes self-attention and employs cross-attention mechanisms. CATS enhances long-term forecasting accuracy by introducing future horizon-dependent parameters, enhanced parameter sharing, and query-adaptive masking. Experiments across various datasets demonstrate that CATS outperforms existing models, such as PatchTST and TimeMixer, in terms of lower mean squared error, fewer parameters, and reduced memory usage. The model's simplicity and improved efficiency make it a strong alternative, especially for long input sequences, while also providing insights into prediction processes through attention maps. The study challenges the dominance of complex Transformer-based approaches and suggests that more streamlined architectures can achieve state-of-the-art performance in time series forecasting.
Mind map
Scalability with input sequence length
Speed and resource consumption tests
Query-adaptive masking mechanism
Enhanced parameter sharing
Future horizon-dependent parameters
Description of Cross-Attention-only Transformer
Insights into prediction processes
Visualization of attention patterns
Efficiency Analysis
CATS Architecture
Data preprocessing techniques (if applicable)
Selection of diverse time series datasets
Investigate efficiency in long-term forecasting
Challenge the need for complex Transformers
To evaluate CATS' performance
Importance of self-attention in sequence analysis
Evolution of time series forecasting models
The value of streamlined architectures in the field
CATS as a strong alternative for time series forecasting
Summary of findings
Future research directions
Limitations and potential improvements
Implications for the dominance of complex Transformers
Memory and computational efficiency benefits
Comparative analysis with state-of-the-art models
CATS' superior performance in forecasting accuracy
Attention Map Interpretation
Model Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results
Method
Introduction
Outline
Introduction
Background
Evolution of time series forecasting models
Importance of self-attention in sequence analysis
Objective
To evaluate CATS' performance
Challenge the need for complex Transformers
Investigate efficiency in long-term forecasting
Method
Data Collection
Selection of diverse time series datasets
Data preprocessing techniques (if applicable)
Data Preprocessing
Handling missing values and normalization
Resampling and feature engineering (if needed)
CATS Architecture
Description of Cross-Attention-only Transformer
Future horizon-dependent parameters
Enhanced parameter sharing
Query-adaptive masking mechanism
Model Evaluation
Performance metrics (MSE, parameters, memory usage)
Comparison with PatchTST and TimeMixer
Experiment design and replication
Efficiency Analysis
Speed and resource consumption tests
Scalability with input sequence length
Attention Map Interpretation
Visualization of attention patterns
Insights into prediction processes
Results
CATS' superior performance in forecasting accuracy
Comparative analysis with state-of-the-art models
Memory and computational efficiency benefits
Discussion
Implications for the dominance of complex Transformers
Limitations and potential improvements
Future research directions
Conclusion
Summary of findings
CATS as a strong alternative for time series forecasting
The value of streamlined architectures in the field

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the effectiveness of self-attentions in time series forecasting by proposing a new cross-attention-based architecture that eliminates self-attentions . This study introduces a novel forecasting architecture called Cross-Attention-only Time Series transformer (CATS) that focuses on cross-attentions and future horizon-dependent parameters as queries, aiming to enhance parameter sharing and improve long-term forecasting performance . While the issue of self-attentions in time series forecasting has been previously noted , this paper contributes by proposing a specific architecture that eliminates self-attentions and emphasizes cross-attentions, offering a new approach to the problem .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that cross-attention-based architecture in Transformer models is more effective for time series forecasting compared to using self-attentions . The study explores the advantages of Transformer models by emphasizing the significance of cross-attention mechanisms in handling the complexities of long-term forecasting tasks . The research provides insights into the reevaluation of self-attentions in time series forecasting and encourages further investigation into the efficacy and efficiency of attention mechanisms across various time series analysis tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel approach for time series forecasting by introducing a new cross-attention-based architecture that removes self-attentions . This model aims to establish a strong baseline for forecasting tasks and offers insights into the complexities of long-term forecasting problems . The proposed architecture consists of three key components:

  • Cross-Attention with Future as Query
  • Parameter Sharing across Horizons
  • Query-Adaptive Masking .

By leveraging advanced architectural designs of time series transformers, the model prioritizes cross-attention without self-attention, which linear models cannot utilize effectively . The study emphasizes the importance of maintaining the periodic properties of time series data while utilizing the structural advantages of the transformer architecture .

Furthermore, the paper highlights the efficiency of the proposed model through parameter sharing and demonstrates significant efficiency through parameter sharing, where the input length doubles, showcasing the model's capability for parameter sharing and efficiency . The research also addresses the challenge of channel independence between variables in time series data and suggests future research to address cross-variate dependency with reduced computation complexity based on the proposed architecture . The proposed model in the paper introduces a novel approach for time series forecasting by prioritizing cross-attention without self-attention, leveraging advanced architectural designs of time series transformers . This model not only preserves the temporal information of time series data similar to linear models but also utilizes the structural advantages of the transformer architecture, maintaining the periodic properties of time series data . The architecture consists of three key components: Cross-Attention with Future as Query, Parameter Sharing across Horizons, and Query-Adaptive Masking, which collectively enhance forecasting performance .

Compared to previous methods, the proposed model, named Cross-Attention-only Time Series transformer (CATS), simplifies the original Transformer architecture by eliminating all self-attentions and focusing on the potential of cross-attentions . By establishing future horizon-dependent parameters as queries and treating past time series data as key and value pairs, the model enhances parameter sharing and improves long-term forecasting performance . CATS demonstrates superior forecasting performance with lower mean squared error, even for longer input sequences, and requires fewer parameters than existing models, showcasing its efficiency and effectiveness .

Additionally, the paper highlights the effectiveness of cross-attention in the proposed structure by comparing it with self-attention layers. The study confirms that the cross-attention mechanism outperforms self-attention layers in most cases, emphasizing the advantages of prioritizing cross-attention for time series forecasting tasks . This comparison underscores the model's capability to achieve higher performance with a more efficient structure by focusing on cross-attentions instead of self-attentions .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of time series forecasting using Transformer models. Noteworthy researchers in this area include:

  • Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam .
  • Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski .
  • Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger .
  • Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan .
  • Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang .
  • Tian Zhou, Ziqing Ma, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, et al. .
  • Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, and JUN ZHOU .
  • Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi .
  • Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long .
  • Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long .
  • Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe Lin, and Alan Yuille .
  • Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu .
  • Haokui Zhang, Wenze Hu, and Xiaoyu Wang .
  • Yunhao Zhang and Junchi Yan .

The key to the solution mentioned in the paper involves developing a new cross-attention-based architecture for time series forecasting by removing self-attentions. This model establishes a strong baseline for forecasting tasks and offers insights into the complexities of long-term forecasting problems. The proposed architecture prioritizes cross-attention without self-attention, leveraging advanced designs of time series transformers, such as cross-attention with future as query, parameter sharing across horizons, and query-adaptive masking .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific settings and parameters to evaluate the effectiveness of self-attentions for time series forecasting . The experimental settings included different datasets such as Weather, Traffic, Electricity, and ETT datasets . These datasets were chosen for their periodic characteristics and real-world relevance in forecasting tasks . The experiments utilized fixed hyperparameters, including a random seed of 2021 for reproducibility, input sequence length L = 96, and forecasting horizon T values of [96, 192, 336, 720] . The model configurations involved using the GeGLU activation function, learnable positional embedding parameters, and three cross-attention layers with specific embedding size and number of attention heads . Additionally, the experiments compared the performance of models with different attention mechanisms, such as cross-attention and self-attention layers, to assess their impact on forecasting accuracy . The results of these experiments provided insights into the effectiveness of cross-attention mechanisms in improving prediction accuracy by leveraging periodic information within the time series data .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on self-attentions for time series forecasting includes seven datasets: Weather, Traffic, Electricity, and ETT (ETTh1, ETTh2, ETTm1, ETTm2) . The datasets capture various periodic characteristics and scenarios relevant for long-term time series forecasting tasks. However, the information about whether the code is open source is not explicitly mentioned in the provided context. For details regarding the availability of the code, it is recommended to refer to the original source of the study or contact the authors directly for clarification.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified in the context of time series forecasting using self-attentions and cross-attentions . The paper extensively evaluates various models and their performance metrics across different datasets, such as Weather, Electricity, Traffic, and ETT datasets, demonstrating the effectiveness of the proposed models in handling time series forecasting tasks.

The experiments include detailed comparisons of models with unified hyperparameter settings, showcasing the performance of different forecasting models in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) . Additionally, the paper explores the impact of attention mechanisms, specifically cross-attention and self-attention layers, on the forecasting accuracy of the models . The results consistently highlight the superiority of cross-attention mechanisms over self-attention layers in improving forecasting performance across various datasets and input sequence lengths .

Furthermore, the paper provides in-depth analyses of the attention mechanisms' ability to capture periodic patterns in time series data, enhancing the interpretability and predictive performance of the models . The visualizations of cross-attention score maps and forecasting results illustrate how the models leverage periodic information to make accurate predictions . These visualizations help in understanding how the models detect shocks and periodic components within the time series data, contributing to the validation of the scientific hypotheses.

Overall, the comprehensive experimental results, performance comparisons, and visualizations presented in the paper offer substantial evidence to support the scientific hypotheses related to the effectiveness of self-attentions and cross-attentions for time series forecasting tasks. The analyses conducted in the paper contribute to advancing the understanding of attention mechanisms in time series forecasting and provide a solid foundation for future research in this domain.


What are the contributions of this paper?

The paper makes significant contributions to the field of time series forecasting by:

  • Introducing a new cross-attention-based architecture that removes self-attentions, establishing a strong baseline for forecasting tasks and offering insights into long-term forecasting complexities .
  • Providing a reevaluation of self-attentions in time series forecasting and highlighting the importance of assessing efficacy and efficiency across different time series analysis tasks .
  • Addressing the limitation of assuming channel independence between variables by proposing methods to handle cross-variate dependency with reduced computation complexity based on the developed architecture .

What work can be continued in depth?

Further research in the field of time series forecasting can be extended by critically assessing the efficacy and efficiency of Transformer architecture across various time series analysis tasks . Additionally, future studies can focus on addressing cross-variate dependency with reduced computation complexity based on proposed architectures to better handle the highly correlated nature of real-world time series data .

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