針對個別使用者從其少量趨勢線樣本生成個人化趨勢線

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：19

、訪客IP：18.226.214.156

姓名

郭同益(Tong-Yi Kuo) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

針對個別使用者從其少量趨勢線樣本生成個人化趨勢線
(Generating Personalized Trend Line Based on Few Labelings from One Individual)

相關論文

★ 透過網頁瀏覽紀錄預測使用者之個人資訊與性格特質	★ 透過矩陣分解之多目標預測方法預測使用者於特殊節日前之瀏覽行為變化
★ 動態多模型融合分析研究	★ 擴展點擊流：分析點擊流中缺少的使用者行為
★ 關聯式學習：利用自動編碼器與目標傳遞法分解端到端倒傳遞演算法	★ 融合多模型排序之點擊預測模型
★ 分析網路日誌中有意圖、無意圖及缺失之使用者行為	★ 基於自注意力機制產生的無方向性序列編碼器使用同義詞與反義詞資訊調整詞向量
★ 探索深度學習或簡易學習模型在點擊率預測任務中的使用時機	★ 空氣品質感測器之故障偵測--基於深度時空圖模型的異常偵測框架
★ 以同反義詞典調整的詞向量對下游自然語言任務影響之實證研究	★ 結合時空資料的半監督模型並應用於PM2.5空污感測器的異常偵測
★ 藉由權重之梯度大小調整DropConnect的捨棄機率來訓練神經網路	★ 使用圖神經網路偵測 PTT 的低活躍異常帳號
★ 基於雙變量及多變量貝他分布的兩個新型機率分群模型	★ 一種可同時更新神經網路各層網路參數的新技術— 採用關聯式學習及管路化機制

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

時間序列資料的大致走向通常稱之為「趨勢線」，然而趨勢線未有精準描述的定義，每個人心中對趨勢線的形狀認知有些許差異，難以用一種趨勢線滿足所有人。另外個別使用者可能也不容易清楚敘述其心中的趨勢線樣貌。
本論文提出一個框架讓個別使用者以「手繪」的方式在十張時間序列資料上標出他認定的趨勢線，讓機器學習模型從中學習該使用者心中的趨勢線樣貌，以應用在其他時間序列資料上。

摘要(英)

The tendency of a time series is usually referred to as a “trend line”. However, the precise definition of a trend line is still ambiguous. Given a time series, different users may come up with varying shapes of trend lines – some may prefer smooth lines, while others may hope the trend line responds to local turbulence. Therefore, a single trend line definition is challenging to meet everyone’s needs. Meanwhile, it could be complicated for users to clearly describe the requirements of a trend line in their minds.
This thesis proposes a framework to learn the customized trend lines that meet users’ demands. First, the framework asks users to plot the expected trend lines on ten time-series datasets. The framework then learns users’ preferred shapes and automatically draws the customized trend lines for other time-series datasets.

關鍵字(中)

★ 時間序列
★ 小樣本
★ 趨勢線
★ 時間序列預測

關鍵字(英)

★ time series
★ small sample
★ trend line
★ time series prediction

論文目次

目錄
頁次
摘要 iv
Abstract v
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 xi
一、緒論 1
1.1 研究動機 .................................................................. 1
1.2 方法簡介 .................................................................. 2
1.3 研究貢獻 .................................................................. 2
1.4 論文架構 .................................................................. 3
二、趨勢線定義相關研究 4
2.1 L1 Trend Filtering, HP Filtering .................................... 4
2.2 STL......................................................................... 7
2.3 RobustSTL ............................................................... 9
三、資料集跟模型介紹 12
3.1 Yahoo S5 資料集 ........................................................ 12
3.2 生成模擬使用者作為資料 ............................................. 12
3.3 學習個人化趨勢線 ...................................................... 14
四、實驗結果 18
4.1 實驗比較之模型介紹 ................................................... 18
4.1.1 非個人化趨勢線 ................................................ 18
4.1.2 使用神經網路模型學習個人化趨勢線 ..................... 19
4.2 訓練及測試用資料集 ................................................... 20
4.3 評量標準 .................................................................. 23
4.3.1 均方誤差 ......................................................... 23
4.3.2 對稱性平均絕對百分比誤差 ................................. 23
4.4 實驗一：MSE 和 SMAPE 評量結果................................ 23
4.5 實驗二：受測者主觀模型評分 ....................................... 29
4.6 個案討論 .................................................................. 33
4.7 使用者混成比例討論 ................................................... 40
五、總結 42
5.1 結論 ........................................................................ 42
5.2 未來展望 .................................................................. 42
六、附錄 44
參考文獻 49

參考文獻

[1] H. Musbah, H. H. Aly, and T. A. Little, “A novel approach for seasonality and trend detection using fast fourier transform in box-jenkins algorithm,” in 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2020, pp. 1–5. doi: 10.1109/CCECE47787.2020.9255819.
[2] Y. R and S. Swamy, “Recent trends in time series forecasting-a survey,” vol. 7, pp. 5623–5628, Apr. 2020.
[3] J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, “Detecting trend and seasonal changes in satellite image time series,” Remote Sensing of Environment, vol. 114, no. 1, pp. 106–115, 2010, issn: 0034-4257. doi: https://doi.org/10. 1016/j.rse.2009.08.014. [Online]. Available: https://www.sciencedirect. com/science/article/pii/S003442570900265X.
[4] S.-J. Kim, K. Koh, S. Boyd, and D. Gorinevsky, “Ell_1 trend filtering,” SIAM review, vol. 51, no. 2, pp. 339–360, 2009.
[5] R. Hodrick and E. Prescott, “Postwar u.s. business cycles: An empirical investi- gation,” Journal of Money, Credit and Banking, vol. 29, no. 1, pp. 1–16, 1997. [Online]. Available: https://EconPapers.repec.org/RePEc:mcb:jmoncb:v:29: y:1997:i:1:p:1-16.
[6] R. B. Cleveland, W. S. Cleveland, J. E. McRae, and I. Terpenning, “Stl: A seasonal- trend decomposition,” J. Off. Stat, vol. 6, no. 1, pp. 3–73, 1990.
[7] Q. Wen, J. Gao, X. Song, L. Sun, H. Xu, and S. Zhu, “Robuststl: A robust seasonal- trend decomposition algorithm for long time series,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 2019, pp. 5409–5416.
[8] J. L. Ticknor, “A bayesian regularized artificial neural network for stock market forecasting,” Expert systems with applications, vol. 40, no. 14, pp. 5501–5506, 2013.
[9] K.-j. Kim, “Financial time series forecasting using support vector machines,” Neu- rocomputing, vol. 55, no. 1-2, pp. 307–319, 2003.
[10] S. Babar and R. H, “Analysis of south west monsoon rainfall trend using statistical techniques over nethravathi basin,” International Journal of Advanced Technology in Civil Engineering, 2013.
49
參考文獻
[11] A. Baheti and D. Toshniwal, “Trend analysis of time series data using data min- ing techniques,” in 2014 IEEE International Congress on Big Data, IEEE, 2014, pp. 430–437.
[12] G. Mahalakshmi, S. Sridevi, and S. Rajaram, “A survey on forecasting of time series data,” in 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), IEEE, 2016, pp. 1–8.
[13] X. Yan and N. Chowdhury, “A comparison between svm and lssvm in mid-term electricity market clearing price forecasting,” May 2013, pp. 1–4, isbn: 978-1-4799- 0031-2. doi: 10.1109/CCECE.2013.6567685.
[14] N. Laptev, J. Yosinski, L. E. Li, and S. Smyl, “Time-series extreme event forecasting with neural networks at uber,” in International conference on machine learning, sn, vol. 34, 2017, pp. 1–5.
[15] G. P. Zhang and M. Qi, “Neural network forecasting for seasonal and trend time series,” European journal of operational research, vol. 160, no. 2, pp. 501–514, 2005.
[16] J. Park, C. G. Park, and K. Lee, “Simultaneous outlier detection and variable selec- tion via difference-based regression model and stochastic search variable selection,” Communications for Statistical Applications and Methods, vol. 26, pp. 149–161, Mar. 2019. doi: 10.29220/CSAM.2019.26.2.149.
[17] I. Choi, C. G. Park, and K. Lee, “Outlier detection and variable selection via difference based regression model and penalized regression,” Journal of the Korean Data And Information Science Sociaty, vol. 29, pp. 815–825, May 2018. doi: 10. 7465/jkdi.2018.29.3.815.
[18] Z. Liu and M. Hauskrecht, “Learning adaptive forecasting models from irregularly sampled multivariate clinical data,” in Thirtieth AAAI conference on artificial in- telligence, 2016.
[19] Q. Zeng, D. Li, G. Huang, et al., “Time series analysis of temporal trends in the pertussis incidence in mainland china from 2005 to 2016,” Scientific reports, vol. 6, no. 1, pp. 1–8, 2016.
[20] L. Anghinoni, L. Zhao, D. Ji, and H. Pan, “Time series trend detection and forecast- ing using complex network topology analysis,” Neural Networks, vol. 117, pp. 295– 306, 2019.
[21] A. De Livera, R. Hyndman, and R. Snyder, “Forecasting time series with complex seasonal patterns using exponential smoothing,” Journal of the American Statis- tical Association, vol. 106, pp. 1513–1527, Jan. 2010. doi: 10.1198/jasa.2011. tm09771.
[22] C. Sax and D. Eddelbuettel, “Seasonal adjustment by X-13ARIMA-SEATS in R,” Journal of Statistical Software, vol. 87, no. 11, pp. 1–17, 2018. doi: 10.18637/jss. v087.i11.
50

參考文獻
[23] D. F. Findley, B. C. Monsell, W. R. Bell, M. C. Otto, and B.-C. Chen, “New capabil- ities and methods of the x-12-arima seasonal-adjustment program,” Journal of Busi- ness Economic Statistics, vol. 16, no. 2, pp. 127–152, 1998, issn: 07350015. [Online]. Available: http://www.jstor.org/stable/1392565 (visited on 06/12/2022).
[24] A. Dokumentov and R. J. Hyndman, “Str: Seasonal-trend decomposition using regression,” INFORMS Journal on Data Science, 2021.
51

指導教授

陳弘軒(Hung-Hsuan Chen)

審核日期

2022-7-19

推文