Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98479
標題: 有線電視收視率在時間序列上的模型探討之研究
Research on the model of CATV ratings in time series
作者: 張巽能
Hsun-Neng Chang
關鍵字: 收視率;時間序列;ARIMA模型;ARIMA model;ratings;time series
引用: 中文部分 張依雯 (1990)。解構台灣地區電視經營生態與收視率之關聯(未出版之碩士論文)。國立政治大學,臺北市。 張錦華 (2009)。電視新聞收視質指標建構及量測計畫。國家通訊傳播委員會專題研究成果報告(編號:PG9806-0352) ,未出版。 邱慧仙(2017)。大數據應用與收視率調查-機上盒篇。創新傳播與數據智慧實驗室。取自http://shucidi.strikingly.com/blog/85266610ca7 葉廣海 (1992)。收視率的三角習題-傳播線上的省思。臺北市,正中書局。 林照真 (2009)。收視率新聞學-台灣電視新聞商品化。臺北市,聯經出版事業股份有限公司。 蔡秀玲 (2000)。無線電視台八點檔連續劇行銷研究--以「台灣廖添丁」「還珠格格二」「土地公傳奇」「狀元親家」為研究對象(未出版之碩士論文) 。國立政治大學,臺北市。 邱慧仙 (2013)。數位時代電視收視率量測機制變革(博士論文)。世新大學,臺北市。 葉小蓁 (1998)。時間序列分析與應用。臺北市,萬達打字印刷有限公司。 英文部分 Beville, H. M. Jr. (1988). Audience ratings:Radio, television and Cable. Hillsdale, New Jersey: Lawrence Erlbaum Associates. Box, G. E. P. , & Jenkins, G. M. (1976). Time Series Analysis Forecasting and Control,revised edition,Holden Day,San Francisco. Buzzard, K. (1992). Electronic media ratings: Turning audiences into dollars and sense.Boston,MA:The Focal Press. Ellmore, R.T. (1990). NTS's Mass Media Dictionary. Lincolnwood,IL: National Textbook Company. Mosco,V. (1996). The Political Economy Of Communication:Rethinking and Renewal. California:SAGE Publications Ltd. Surmanek, J. (2003). Advertising Media:A to Z. New York:McGraw-Hill Education.
摘要: 
收視率這個令電視台與廣告商眼睛為之一亮的數字,在這個資訊爆炸的時代,觀眾有各種閱聽的管道,以現在一百多台的電視節目,對照台灣的收視人口,廣告商需要以各個節目在特定時段的收視率來做廣告的配置,通常收視率越高代表能收到的廣告費也越多,因此造就收視率成為電視台與廣告商談判的籌碼。
本研究考慮以下條件並搭配ARIMA模型進行收視率預測:
1.樣本即母體
2.以收視佔有率代替收視率
3.以每半小時為單位
本研究取2017年1月到6月娛樂類別的收視率,採AIC、BIC準則找出四組ARIMA季節性相乘模型,從中選出適配度最好的模型,經後續分析與驗證,其適配結果有不錯的表現。

The ratings, which makes the TV station and advertiser's eyes one of the numbers, provides viewers with a variety of audio channels in the age of information ex-plosion, in contrast to the current more than 100 TV programs, compared to Taiwan's viewership, advertisers need to advertise their ads at a given period of time. The higher the audience rating is, the more advertising fees the advertisers can receive. Therefore, audience ratings become the bargaining chip of TV sta-tions and advertisers.

This study considers the following conditions and matches the ARIMA model to predict audience ratings:
1. sample is the mother body.

2. instead of the ratings by viewing possession.

3. per half hour.

This study takes the ratings of entertainment categories from January to June 2017, and uses AIC and BIC criteria to find four groups of ARIMA seasonal multiplication models, from which the best model of adaptation is selected. After the follow-up analysis and verification, the results have a good performance.
URI: http://hdl.handle.net/11455/98479
Rights: 同意授權瀏覽/列印電子全文服務,2021-08-21起公開。
Appears in Collections:應用數學系所

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