A study of combination of forecasts

Pao Yue-kong Library Electronic Theses Database

A study of combination of forecasts

 

Author: Wong, Chi-shing
Title: A study of combination of forecasts
Degree: M.Sc.
Year: 2000
Subject: Forecasting -- Mathematical models
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Applied Mathematics
Pages: vi, 118 : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1535434
URI: http://theses.lib.polyu.edu.hk/handle/200/3005
Abstract: The objective of this project is to investigate the performance of conventional combined forecast methods and proposed new combined forecast methods. The new methods proposed use a balance factor calculated from three different ways (denoted by B1, B2 and B3) to compensate the bias presented in the individual forecast. In addition, skewness factor was proposed to handle the error pattern for weight computation. Twenty time series were collected and used for the investigation. Four individual forecasts were selected for combination. They were (1) Box-Jenkins method, (2) Holt-Winters method, (3) adaptive-response-rate simple exponential smoothing method and (4) double moving average method. Eight conventional combined forecast methods were selected from the past researches. With different settings of parameters, these eight methods expanded to 52 methods for investigation. Also the proposed new methods using balance factor and skewness factor provided 21 methods for investigation. According to the findings of this study, the best conventional methods provided a U-statistic and MAPE of 0.877 and 5.60% respectively. However, it did not out perform the ARIMA model which U-statistic and MAPE were 0.875 and 5.41% respectively. The proposed skewness factor was found successful to provide a slightly improved performance. However, it is not recommended to apply for forecasting work if cost and effectiveness are considered. The proposed balance factor from method B1 were found successful to provide better performance than conventional methods on a condition that this method applies only to the individual forecasts in which autocorrelation of residuals is present. The best U-statistic and MAPE of this method were 0.858 and 5.16% respectively. The proposed balance factor from method B2 was not successful. The proposed balance factor from method B3 was found successful and the best U-statistic and MAPE were 0.867 and 5.55%. However, unlike the case of method B1, the autocorrelation of residuals does not have significant effect on its application.

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