隨著我們使用的科技日益精進,寬頻顯然成為重要的工具,相對的,寬頻在未來的成長與發展,也會對國家帶來極大極的效益。本研究是根據國家通訊傳播委員會(NCC)之資料,先收集近十年用戶數的資料,再將寬頻分為固網寬頻與行動寬頻進行預測研究,最後透過時間序列的準則建立預測模型。 研究方法是使用單根檢定(ADF test)、JB檢定、Q檢定、Q2檢定進行預測模型的診斷,確認預測模型是屬於ARIMA、ARCH或GARCH,最後以MAPE對預測模型進行衡量績效,其研究結果歸納以下幾點: 1. 因固網寬頻與行動寬頻存在非常態現象,因此在進行時間數列分析之前,所有資料先經一階差分處理。 2. 經預測模型分析,固網寬頻可配適為GARCH(1,1),及行動寬頻可配適為ARCH(1)模型。 3. 固網寬頻和行動寬頻的MAPE(Mean Absolute Percentage Error平均誤差百分比值)結果表示固網寬頻和行動寬頻皆為合理模型。 建議:固網和行動寬頻皆為合理預測模型,可提供未來預測成長趨勢使用,但因為資料數據來源有限,後續可持續收集數據資料,做更精準的預測模型。
As the technology we use daily becomes more advanced, the broadband has become the important tool obviously. Of course, the broadband growth and development will bring the great benefit to a country in the future. The data of the study is from National Communications Commission(NCC). First, collect the number of broadband account over the past decade. And then, divide the “Broadband” into “Broadband” and “Mobile Broadband” to do the forecast research. Finally, is based on the principle of “The Time Series Analysis Study” to build the forecast model. The research method is to use ADF test, JB test, Q test and Q2 test to diagnose the forecast model. And then, checking the forecast model belongs to ARIMA, ARCH or GARCH. Finally, is based on the MAPE principle to measure the performance of forecast model. The research results are summarized as below: 1. The broadband and mobile broadband exist non-stationary, so all of the data need to do the “first difference” before “The Time Series Analysis” process. 2. After forecast model analysis, the Broadband forecast model is GARCH(1,1), and the Mobile Broadband forecast model is ARCH(1). 3. The research result of the MAPE(Mean Absolute Percentage Error) value of Broadband and Mobile Broadband are “acceptable model”. Suggestion: The Broadband and Mobile Broadband are acceptable forecasting model. They could be used for forecasting in the future, but the original data is limited. For further research, still need to keep collecting data to do the better forecasting model.