Applying Bayesian Statistics in Establishing Time-series Models to Predict Customers' Consuming Behavior
Date Issued
2006
Date
2006
Author(s)
Hu, Jyun-Jie
DOI
zh-TW
Abstract
With the development of the concept of customer relationship management, it is the key point on how marketers to regain the initiative and make research on the useful information derived from the first-hand customer database, and to serve it as a basis for the development of various marketing strategies. As the database techniques mature, every consumer’s consuming behavior may be recorded and analyzed elaborately. Therefore, the purpose of this study is to analyze the consumers’ previous consumption records, and try to establish time-series models to forecast the potential consuming behavior in the absence of exogenous variables involved.
This research targets at the database of one of the leading domestic mobile telephone operators, randomly selects 1,000 customers’ sample and the phone records called for a period of 34 weeks, and analyzes and predicts the percentage of the total amount of calls the consumer phoned via different telecommunication operators. This research adopts the Autoregressive Moving Average Model (ARIMA) model and Vector Autoregressive Model (VAR) model. Besides, it also applies two kinds of prior distribution methods in Bayesian Analysis, which are Minnesota priori distribution and Non-informative standard Jeffrey's priori distribution, to establish different Bayesian Vector Autoregressive (BVAR) model. And finally, by using the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE), we compare the performance of different models inside or outside the samples.
The study finds out that if integratively consider the predictive ability and the substantive feasibility, the Bayesian Vector Autoregressive Model using the Minnesota prior performs better than the other models. However, the domestic research on Bayesian Vector Autoregressive Models is comparatively lacking. Thus, if future studies can make the appropriate settings within the parameters of the model, we believe the predictive ability will be further improved.
Subjects
預測
時間序列
自我迴歸整合移動平均
向量自我迴歸
貝氏向量自我迴歸
forecast
time-series
ARIMA
VAR
BVAR
Type
thesis
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