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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54823完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林守德 | |
| dc.contributor.author | Ting-Wei Ku | en |
| dc.contributor.author | 顧廷緯 | zh_TW |
| dc.date.accessioned | 2021-06-16T03:39:15Z | - |
| dc.date.available | 2015-03-16 | |
| dc.date.copyright | 2015-03-16 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-02-24 | |
| dc.identifier.citation | 1. Death Of TV, http://www.businessinsider.com/category/death-of-tv
2. Danaher, P.J., Dagger, T.S., Smith, M.S.: Forecasting television ratings. International Journal of Forecasting 27(4), 1215–1240 (2011) 3. Danaher, P., Dagger, T.: Using a nested logit model to forecast television ratings. International Journal of Forecasting 28(3), 607–622 (2012) 4. Cheng, Y.H., Wu, C.M., Ku, T., Chen, G.D.: A predicting model of TV audience rating based on the Facebook. International Conference on Social Computing (SocialCom), pp. 1034–1037. IEEE (2013) 5. Hsieh, W.T., Chou, S.C.T., Cheng, Y.H., Wu, C.M.: Predicting TV audience rating with social media. Proceedings of the IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 1–5. Asian Federation of Natural Language Processing, Nagoya (2013) 6. Yu-Yang Huang, Yu-An Yen, Ting-Wei Ku, Shou-De Lin, Wen-Tai Hsieh, Tsun Ku: A Weight-Sharing Gaussian Process Model Using Web-Based Information for Audience Rating Prediction. TAAI, LNAI 8916, pp. 198-208 (2014) 7. C.Meek, D.M. Chichering, D. Heckerman: Autoregressive Tree Models for Time-Series Analysis. Proceedings of the Second International SIAM Conference on Data Mining, pp. 229-244 (2002) 8. Leo Breiman: Bagging predictors. Machine Learning Volume 24, Issue 2, pp, 123-140 (1996) 9. H Drucker: Improving regressors using boosting techniques. ICML (1997) 10. Brown, Robert G.: Exponential Smoothing for Predicting Demand. Cambridge, Massachusetts: Arthur D. Little Inc. p. 15 (1956) 11. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. R version 3.1.2. (2014) 12. C. C. Holt: Forecasting trends and seasonals by exponentially weighted moving averages, ONR Research Memorandum, Carnegie Institute of Technology 52 (1957) 13. Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D: Forecasting with exponential smoothing: the state space approach, Springer-Verlag (2008) 14. Box, George, Jenkins, Gwilym: Time series analysis: Forecasting and control. San Francisco: Holden-Day (1970) 15. Hyndman, R.J. and Khandakar, Y.: Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26(3). R package version 5.7. (2008) 16. Terry Therneau, Beth Atkinson and Brian Ripley: rpart: Recursive Partitioning and Regression Trees. R package version 4.1-8. (2014) 17. Terry M. Therneau, Elizabeth J. Atkinson, Mayo Foundation: An Introduction to Recursive Partitioning Using the RPART Routines. (2015) | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54823 | - |
| dc.description.abstract | 此論文主要貢獻為提出一個簡單且實驗結果準確的電視收視率預測方法，名為 Time Weighting Regression (TWR)。基於「越新的資料對預測接下來的收視率越重要」的假設，TWR 主要做的事情為：根據資料的時間賦予權重，再以帶有權重的資料建立回歸模型，最後用建立的模型預測接下來的收視率。我們以真實世界的電視收視率資料進行實驗，結果顯示它的預測比知名的時間序列模型（例如 Exponential Smoothing 和 ARIMA）和回歸模型（類神經網路）還準。 | zh_TW |
| dc.description.abstract | In this thesis, the primary contribution is proposing a simple and experimentally accurate solution, named Time Weighting Regression (TWR), to the problem of TV ratings prediction. Based on the assumption that newer data are more important for predicting upcoming ratings, what TWR does is: weighing data based on time, and then using weighted data to build regression model for predicting upcoming ratings. In the experiments on a real-world TV ratings data set, it outperforms well-known time series models (e.g., Exponential Smoothing and ARIMA) and regression model (neural network). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T03:39:15Z (GMT). No. of bitstreams: 1 ntu-104-R01922060-1.pdf: 1954873 bytes, checksum: c61d8c5cd2eed9148bb1c948a20a9aca (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 誌謝 2
中文摘要 3 英文摘要 3 圖目錄 5 表目錄 5 第一章 Introduction 6 1.1 Contribution 6 1.2 Background and importance of problem 6 1.3 Overview of problem and solution 6 1.4 Motivation of solution 7 第二章 Related Works 7 2.1 TV ratings prediction 7 2.2 Models compared to TWR in experiments 9 第三章 Method 11 3.1 Pseudo-code of TWR 11 3.2 Fitting step 1: Windowing transformation 12 3.3 Fitting step 2: Weighing training instances 12 3.4 Fitting step 3: Building a base model 13 3.5 Predicting stage of TWR 13 第四章 Experiments 14 4.1 Data set 14 4.2 Evaluation metric 16 4.3 TWR settings and implementation 16 4.4 Results 16 4.5 Discussion 18 第五章 Conclusion 19 第六章 Future Work 19 參考文獻 References 19 附錄 Appendix 21 A: Equations of 30 state space models for ETS 21 B: Equations of neural network auto-regression 22 C: Equations of TWR 22 | |
| dc.language.iso | en | |
| dc.subject | 回歸 | zh_TW |
| dc.subject | 時間序列預測 | zh_TW |
| dc.subject | 電視收視率預測 | zh_TW |
| dc.subject | regression | en |
| dc.subject | time series prediction | en |
| dc.subject | TV ratings prediction | en |
| dc.title | 以基於時間比重之回歸預測電視收視率 | zh_TW |
| dc.title | TV Ratings Prediction with Time Weighting Based Regression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭卜任,李政德 | |
| dc.subject.keyword | 時間序列預測,電視收視率預測,回歸, | zh_TW |
| dc.subject.keyword | time series prediction,TV ratings prediction,regression, | en |
| dc.relation.page | 22 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-02-24 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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