Open AccessForecasting the Daily Transaction Data Utilizing a Day of the Week Index in the Case of Web Site
Author(s) -
Kazuhiro Takeyasu,
Tomohiro Watanabe
Publication year - 2018
Publication title -
international journal of business administration
Language(s) - English
Resource type - Journals
eISSN - 1923-4015
pISSN - 1923-4007
DOI - 10.5430/ijba.v9n5p11
Subject(s) - exponential smoothing , smoothing , computer science , constant (computer programming) , variance (accounting) , database transaction , index (typography) , moving average , autoregressive–moving average model , data mining , simple (philosophy) , mathematical optimization , econometrics , algorithm , mathematics , database , autoregressive model , philosophy , accounting , epistemology , world wide web , business , computer vision , programming language
The daily web site access data’s forecasting is an important factor for the web master in order to improve the quality of the site, and to increase the sales from the site. In this paper, the authors propose a new method to improve forecasting accuracy and confirm them by the Web transaction data (Nail consulting site). Focusing that the equation of exponential smoothing method (ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Generally, smoothing constant is selected arbitrarily. But in this paper, we utilize above stated theoretical solution. Firstly, the authors make estimation of ARMA model parameter and then estimate smoothing constants. Thus theoretical solution is derived in a simple way and it may be utilized in various fields. Combining the trend removing method with this method, we aim to improve forecasting accuracy. Furthermore, “a day of the week index” is newly introduced for the daily data and we have obtained good result. The effectiveness of this method should be examined in various cases.