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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/158370
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158370


    Title: 基於擴散式資料增強與SimSiam架構之時間序列自監督表示學習研究
    Diffusion-Augmented Contrastive Representation Learning for Time-Series Forecasting
    Authors: 賴皓千
    Lai, Hao-Chien
    Contributors: 蔡炎龍
    Tsai, Yen-Lung
    賴皓千
    Lai, Hao-Chien
    Keywords: 擴散模型
    對比學習
    時間序列
    資料增強
    股票市場
    正樣本生成
    模式一致性
    結構保留
    無監督學習
    回報預測
    異常檢測
    Diffusion Models
    Contrastive Learning
    Time-Series Data
    Data Augmentation
    Stock Market
    Positive Sample Generation
    Pattern Consistency
    Structural Preservation
    Unsupervised Learning
    Return Prediction
    Anomaly Detection
    Date: 2025
    Issue Date: 2025-08-04 13:10:41 (UTC+8)
    Abstract: 在對比學習(Contrastive Learning)中,資料增強是生成正樣本的關鍵手段,對模型效果有著重要影響。在圖像數據中,常見的增強方法如裁剪、翻轉等可以生成有效的正樣本,但在時間序列數據中,這些方法可能破壞數據的時序結構及內部關係,導致模型學習效果下降。儘管擴散模型(Diffusion Models)已成為時間序列數據分析與預測的有效工具,但其在對比學習資料增強中的應用尚未被廣泛討論,部分原因在於傳統的擴散模型生成過程多依賴隨機採樣,難以生成與特定數據對應的正樣本。
    為解決這一挑戰,本研究設計了一種針對時間序列數據的擴散模型應用手法,摒棄傳統隨機採樣策略,通過重新編輯數據生成具有模式一致性和結構保留的正樣本,並將其應用於對比學習框架。實驗結果表明,該方法在台灣股票市場數據上的應用顯著提升了模型的特徵表徵能力,在回報預測和異常檢測等下游任務中展現出優越性能,尤其是在資料稀缺或不平衡的情境下效果尤為顯著。本研究不僅填補了擴散模型在對比學習中的研究空白,還為時間序列數據的資料增強提供了一種新穎的解決方案。
    Data augmentation is a critical component in contrastive learning (CL) for generating positive samples, significantly impacting the model’s performance. While common augmentation methods such as cropping and flipping are effective for image data, these approaches often disrupt the temporal structure and relationships in time-series data, leading to suboptimal learning outcomes. Although diffusion models have become powerful tools for analyzing and forecasting time-series data, their application in data augmentation for contrastive learning remains underexplored. One reason is that conventional diffusion model approaches rely on random sampling, which generates points from the data distribution rather than specific positive samples corresponding to existing data.
    To address this limitation, this study proposes a novel approach to applying diffusion models for time-series data. By discarding the traditional random sampling strategy, we utilize a tailored editing process to generate positive samples that preserve pattern consistency and structural integrity. These samples are then integrated into a contrastive learning framework. Experimental results demonstrate that the proposed method significantly enhances feature representation on Taiwan stock market data, achieving superior performance in downstream tasks such as return prediction and anomaly detection, particularly in data-scarce or imbalanced scenarios. This study not only bridges the gap in utilizing diffusion models for contrastive learning but also provides an innovative solution for time-series data augmentation.
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    [2] Chen, X., & He, K. (2021). Exploring simple siamese representation learning. In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition* (pp. 15750-15758).

    [3] Demirel, B. U., & Holz, C. (2023). Finding order in chaos: A novel data augmentation method for time series in contrastive learning. *Advances in Neural Information Processing Systems*, 36, 30750-30783.

    [4] Dhariwal, P., & Nichol, A. (2021). Diffusion models beat gans on image synthesis. *Advances in neural information processing systems*, 34, 8780-8794.

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    [8] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. *Advances in neural information processing systems*, 33, 6840-6851.

    [9] Iwana, B. K., & Uchida, S. (2021). An empirical survey of data augmentation for time series classification with neural networks. *PLOS ONE*, 16(7).

    [10] Jing, B., Wang, Y., Sui, G., Hong, J., He, J., Yang, Y., Li, D., & Ren, K. (2024, October). Automated contrastive learning strategy search for time series. In *Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM '24* (pp. 4612-4620). ACM.

    [11] Kalbande, D., Prabhu, P., Gharat, A., & Rajabally, T. (2021). A fraud detection system using machine learning. In *2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)* (pp. 1-7). IEEE.

    [12] Kong, Z., Ping, W., Huang, J., Zhao, K., & Catanzaro, B. (2021). Diffwave: A versatile diffusion model for audio synthesis.

    [13] Lee, S., Lee, G., Kim, H., Kim, J., & Uh, Y. (2023). Sequential data generation with groupwise diffusion process.

    [14] Lin, L., Li, Z., Li, R., Li, X., & Gao, J. (2024). Diffusion models for time-series applications: a survey. *Frontiers of Information Technology & Electronic Engineering*, 25(1), 19-41.

    [15] Luo, D., Cheng, W., Wang, Y., Xu, D., Ni, J., Yu, W., Zhang, X., Liu, Y., Chen, Y., Chen, H., et al. (2023). Time series contrastive learning with information-aware augmentations. In *Proceedings of the AAAI Conference on Artificial Intelligence* (Vol. 37, pp. 4534-4542).

    [16] Ma, C., & Yan, S. (2022). Deep learning in the chinese stock market: the role of technical indicators. *Finance Research Letters*, 49, 103025.

    [17] Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J. Y., & Ermon, S. (2022). Sdedit: Guided image synthesis and editing with stochastic differential equations.

    [18] Rasul, K., Seward, C., Schuster, I., & Vollgraf, R. (2021). Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In *International conference on machine learning* (pp. 8857-8868). PMLR.

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    [20] Shobayo, O., Adeyemi-Longe, S., Popoola, O., & Ogunleye, B. (2024, October). Innovative sentiment analysis and prediction of stock price using finbert, gpt-4 and logistic regression: A data-driven approach. *Big Data and Cognitive Computing*, 8(11), 143.

    [21] Solis-Martin, D., Galan-Paez, J., & Borrego-Diaz, J. (2023). D3a-ts: Denoising-driven data augmentation in time series.

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    [23] Wang, T., & Isola, P. (2020). Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In *International conference on machine learning* (pp. 9929-9939). PMLR.

    [24] Wang, W., Song, H., Si, S., Lu, W., & Cai, Z. (2024). Data augmentation based on diffusion probabilistic model for remaining useful life estimation of aero-engines. *Reliability Engineering & System Safety*, 252, 110394.

    [25] Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2021, August). Time series data augmentation for deep learning: A survey. In *Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-2021* (pp. 4653-4660). International Joint Conferences on Artificial Intelligence Organization.

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    [27] Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2021). A transformer-based framework for multivariate time series representation learning. In *Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining* (pp. 2114-2124).
    Description: 碩士
    國立政治大學
    應用數學系
    111751016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111751016
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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