Tianxiang Zhan

University of Electronic Science and Technology of China M.S., University of Electronic Science and Technology of China (Now)
Southwest University B.S., Southwest University (2023)

Greetings, I am currently pursuing a Master's degree at the University of Electronic Science and Technology of China, with an anticipated graduation date of June 2026. My research focuses on the fields of time series analysis, statistical physics, information theory, and evidence theory. I am deeply passionate about exploring the intersections within these disciplines and addressing the complex challenges they present.

I am actively seeking a fully-funded Ph.D. position in Computer Science, where I hope to join a vibrant research community and collaborate with like-minded scholars in pushing the boundaries of scientific knowledge. If my research interests align with yours, or if you see potential for a research collaboration, please feel free to reach out to me via email or WeChat (please indicate the purpose of your contact when messaging).

I look forward to connecting with you and am excited about the prospect of collectively advancing the field of science.

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Education
  • University of Electronic Science and Technology of China

    University of Electronic Science and Technology of China

    M.S. in Computer Science Sep. 2023 - Now

  • Southwest University

    Southwest University

    B.S. in Software Engineering Sep. 2019 - Jul. 2023

Experience
  • Chongqing University

    Chongqing University

    Research Intern Sep. 2022 - Now

  • Griffith University

    Griffith University

    Research Intern Oct. 2024 - Now

Reviewer Role
  • Conference and Workshop on Neural Information Processing Systems 2024

    Conference and Workshop on Neural Information Processing Systems 2024

  • International Conference on Learning Representations 2025

    International Conference on Learning Representations 2025

  • International Conference on Artificial Intelligence and Statistics 2025

    International Conference on Artificial Intelligence and Statistics 2025

  • 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing

    2025 IEEE International Conference on Acoustics, Speech, and Signal Processing

  • IEEE Access

    IEEE Access

  • Discover Artificial Intelligence

    Discover Artificial Intelligence

Mentor Role

  1. Jingyou Wu

  2. Juntao Xu

  3. Mengyu Yang

Selected Publications (view all )
A novel weighted approach for time series forecasting based on visibility graph
A novel weighted approach for time series forecasting based on visibility graph

Tianxiang Zhan, Fuyuan Xiao

Pattern Recognition 2024 中科院升级版1区 CCF B

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance.

[Paper] [Code] [Preprint]

A novel weighted approach for time series forecasting based on visibility graph
A novel weighted approach for time series forecasting based on visibility graph

Tianxiang Zhan, Fuyuan Xiao

Pattern Recognition 2024 中科院升级版1区 CCF B

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance.

[Paper] [Code] [Preprint]

Generalized information entropy and generalized information dimension
Generalized information entropy and generalized information dimension

Tianxiang Zhan, Jiefeng Zhou, Zhen Li, Yong Deng

Chaos, Solitons & Fractals 2024 中科院升级版1区

The concept of entropy has played a significant role in thermodynamics and information theory, and is also a current research hotspot. Information entropy, as a measure of information, has many different forms, such as Shannon entropy and Deng entropy, but there is no unified interpretation of information from a measurement perspective. To address this issue, this article proposes Generalized Information Entropy (GIE) that unifies entropies based on mass function. Meanwhile, GIE establishes the relationship between entropy, fractal dimension, and number of events. Therefore, Generalized Information Dimension (GID) has been proposed, which extends the definition of information dimension from probability to mass fusion. GIE plays a role in approximation calculation and coding systems. In the application of coding, information from the perspective of GIE exhibits a certain degree of particle nature that the same event can have different representational states, similar to the number of microscopic states in Boltzmann entropy.

[Paper]

Generalized information entropy and generalized information dimension
Generalized information entropy and generalized information dimension

Tianxiang Zhan, Jiefeng Zhou, Zhen Li, Yong Deng

Chaos, Solitons & Fractals 2024 中科院升级版1区

The concept of entropy has played a significant role in thermodynamics and information theory, and is also a current research hotspot. Information entropy, as a measure of information, has many different forms, such as Shannon entropy and Deng entropy, but there is no unified interpretation of information from a measurement perspective. To address this issue, this article proposes Generalized Information Entropy (GIE) that unifies entropies based on mass function. Meanwhile, GIE establishes the relationship between entropy, fractal dimension, and number of events. Therefore, Generalized Information Dimension (GID) has been proposed, which extends the definition of information dimension from probability to mass fusion. GIE plays a role in approximation calculation and coding systems. In the application of coding, information from the perspective of GIE exhibits a certain degree of particle nature that the same event can have different representational states, similar to the number of microscopic states in Boltzmann entropy.

[Paper]

Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting

Tianxiang Zhan, Yuanpeng He, Zhen Li, Yong Deng, Wenjie Du, Qingsong Wen

Arxiv 2024 Preprint

In real-world scenarios, time series forecasting often demands timeliness, making research on model backbones a perennially hot topic. To meet these performance demands, we propose a novel backbone from the perspective of information fusion. Introducing the Basic Probability Assignment (BPA) Module and the Time Evidence Fusion Network (TEFN), based on evidence theory, allows us to achieve superior performance. On the other hand, the perspective of multi-source information fusion effectively improves the accuracy of forecasting. Due to the fact that BPA is generated by fuzzy theory, TEFN also has considerable interpretability. In real data experiments, the TEFN partially achieved state-of-the-art, with low errors comparable to PatchTST, and operating efficiency surpass performance models such as Dlinear. Meanwhile, TEFN has high robustness and small error fluctuations in the random hyperparameter selection. TEFN is not a model that achieves the ultimate in single aspect, but a model that balances performance, accuracy, stability, and interpretability.

[Official Page] [Preprint] [Code] [PDF]

Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting

Tianxiang Zhan, Yuanpeng He, Zhen Li, Yong Deng, Wenjie Du, Qingsong Wen

Arxiv 2024 Preprint

In real-world scenarios, time series forecasting often demands timeliness, making research on model backbones a perennially hot topic. To meet these performance demands, we propose a novel backbone from the perspective of information fusion. Introducing the Basic Probability Assignment (BPA) Module and the Time Evidence Fusion Network (TEFN), based on evidence theory, allows us to achieve superior performance. On the other hand, the perspective of multi-source information fusion effectively improves the accuracy of forecasting. Due to the fact that BPA is generated by fuzzy theory, TEFN also has considerable interpretability. In real data experiments, the TEFN partially achieved state-of-the-art, with low errors comparable to PatchTST, and operating efficiency surpass performance models such as Dlinear. Meanwhile, TEFN has high robustness and small error fluctuations in the random hyperparameter selection. TEFN is not a model that achieves the ultimate in single aspect, but a model that balances performance, accuracy, stability, and interpretability.

[Official Page] [Preprint] [Code] [PDF]

Random Graph Set and Evidence Pattern Reasoning Model
Random Graph Set and Evidence Pattern Reasoning Model

Tianxiang Zhan, Zhen Li, Yong Deng

Arxiv 2024 Preprint

Evidence theory is widely used in decision-making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern operators and decision making operators, corresponding preferences can be set for different tasks. Random Permutation Set (RPS) expands order information for evidence theory. It is hard for RPS to characterize the complex relationship between samples such as cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types. In order to illustrate the significance of RGS and EPRM, an experiment of aircraft velocity ranking was designed and 10,000 cases were simulated. The implementation of EPRM called Conflict Resolution Decision optimized 18.17\% of the cases compared to Mean Velocity Decision, effectively improving the aircraft velocity ranking. EPRM provides a unified solution for evidence-based decision making.

[Preprint] [Dataset] [PDF]

Random Graph Set and Evidence Pattern Reasoning Model
Random Graph Set and Evidence Pattern Reasoning Model

Tianxiang Zhan, Zhen Li, Yong Deng

Arxiv 2024 Preprint

Evidence theory is widely used in decision-making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern operators and decision making operators, corresponding preferences can be set for different tasks. Random Permutation Set (RPS) expands order information for evidence theory. It is hard for RPS to characterize the complex relationship between samples such as cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types. In order to illustrate the significance of RGS and EPRM, an experiment of aircraft velocity ranking was designed and 10,000 cases were simulated. The implementation of EPRM called Conflict Resolution Decision optimized 18.17\% of the cases compared to Mean Velocity Decision, effectively improving the aircraft velocity ranking. EPRM provides a unified solution for evidence-based decision making.

[Preprint] [Dataset] [PDF]

Differential Convolutional Fuzzy Time Series Forecasting
Differential Convolutional Fuzzy Time Series Forecasting

Tianxiang Zhan, Yuanpeng He, Zhen Li, Yong Deng

IEEE Transactions on Fuzzy Systems 2023 中科院升级版1区 CCF B

Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system, which leads to the loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model differential fuzzy convolutional neural network (DFCNN) utilizes a convolution neural network to reimplement FTSF with learnable ability. DFCNN is capable of recognizing potential information and improving forecasting accuracy. Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system. At the same time, FTSF usually cannot achieve satisfactory performance of nonstationary time series due to the trend of nonstationary time series. The trend of nonstationary time series causes the fuzzy set established by FTSF to be invalid and causes the forecasting to fail. DFCNN utilizes the difference algorithm to weaken the nonstationary time series so that DFCNN can forecast the nonstationary time series with a low error that FTSF cannot forecast in satisfactory performance. After the mass of experiments, DFCNN has an excellent prediction effect, which is ahead of the existing FTSF and common time series forecasting algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds continued research value.

[Paper] [Preprint]

Differential Convolutional Fuzzy Time Series Forecasting
Differential Convolutional Fuzzy Time Series Forecasting

Tianxiang Zhan, Yuanpeng He, Zhen Li, Yong Deng

IEEE Transactions on Fuzzy Systems 2023 中科院升级版1区 CCF B

Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system, which leads to the loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model differential fuzzy convolutional neural network (DFCNN) utilizes a convolution neural network to reimplement FTSF with learnable ability. DFCNN is capable of recognizing potential information and improving forecasting accuracy. Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system. At the same time, FTSF usually cannot achieve satisfactory performance of nonstationary time series due to the trend of nonstationary time series. The trend of nonstationary time series causes the fuzzy set established by FTSF to be invalid and causes the forecasting to fail. DFCNN utilizes the difference algorithm to weaken the nonstationary time series so that DFCNN can forecast the nonstationary time series with a low error that FTSF cannot forecast in satisfactory performance. After the mass of experiments, DFCNN has an excellent prediction effect, which is ahead of the existing FTSF and common time series forecasting algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds continued research value.

[Paper] [Preprint]

All publications