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題名:NGM(1, N)於臺北年貨大街之花生銷售預測
書刊名:觀光與休閒管理期刊
作者:余清昇陳思翰
作者(外文):Yu, Ching-shengChen, Ssu-han
出版日期:2017
卷期:5:特刊1
頁次:頁8-19
主題關鍵詞:年貨大街銷售預測灰色理論基因演算法NGM(1, N)New Year's Grocery StreetSales forecastingNonlinear grey multivariable modelGenetic algorithm
原始連結:連回原系統網址new window
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  • 被引用次數被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:1
  • 共同引用共同引用:0
  • 點閱點閱:10
1996年起台北市政府推動台北年貨大街之活動,一舉將迪化街與年貨大街畫上等號,每逢歲末年初,上迪化街辦年貨幾乎成了北台灣人的全民運動。據迪化街商家表示,在年貨大街活動期間各種商品銷售的量,往往是平時每月銷售量業績的數十倍、甚至百倍,驟升的業績帶來興奮的喜悅,但也伴隨著沉重成本的壓力,貨品若無法在短短15至20天的活動期間內販售完畢,平時銷售量可能因無法攤銷以導致高成本的積壓。因此,要如何透過有限的資訊預測貨品的準備量乃本研究所欲探知的主題。傳統預測方法如天真預測法與移動平均法都僅透過歷史資料對未來做預測,往往會忽略其他的影響因子;而相對較可靠的時間序列預測法、線性迴歸分析以及類神經網路等方法卻只在累積數據較多的情形下才可行;灰預測(Grey Forecasting)除了簡單易懂外,更能在少量數據的情境下進行預測,本研究透過NGM(1, N)之非線性多變量灰預測模型進行花生銷售量之預測,以公開或私人且少量的多變量數據來預測下一年度之銷售量,以作為進貨量的參考。此外,研究中亦引入基因演算法(Genetic Algorithm)對NGM(1, N)進行超參數微調(Hyper-parameter Tuning)與變數選擇(Feature Selection),以減少模式執行時的人為干擾。
In 1996, the "Taipei New Year's Grocery Street", which was promoted by the Taipei City Government, had combine Dihua street with the "Taipei New Year Market (TNYM)" together. When to time has come to the end of year, citizens whom live in north part of Taiwan will visit Dihua street and prepare groceries for the New Year. Groceries, which had traded during the TNYM, had created more than ten times of profit. Sellers said, "Happiness and pressure were two feelings that brought to sellers during this event." The quantity of commodities, the cost of commodities, and the time were major problems for sellers need to be concern. So, for sellers to predict the number of commodities before the event, which only had short period, well be the major subject in this essay. Traditional predictive methods such as Naive Forecasting and Moving Averages are all method for extend predictors to the historical data, but those methods will neglect the influence factors that are not mention in historical data. Time Series Forecasting, Multiple Regression Analysis and Artificial Neural Network are more reliable methods that only can be use and comply in larger dataset. The grey multivariable model is a simple and easy to understand method, it is the only method that requires small and incomplete information to do the prediction. This study will use nonlinear grey multivariable model, NGM(1, N) with small amount and readily available multi-variable data to forecast the next period’s sales volume, which will reference for the purchase volume for the event. Besides, this study leases several adjustable hyper-parameters such as the smoothing factor, the initial condition, and power exponents as well as the feature filter and then integrates all of them into the model. Furthermore, the genetic algorithm (GA) is introduced to alleviate the problem of manual selection of those hyper-parameters and features.
期刊論文
1.Hsu, L. C.、Wang, C. H.(2007)。Forecasting the output of integrated circuit industry using a grey model improved by the Bayesian analysis。Technological Forecasting and Social Change,74(6),843-853。  new window
2.Sun, Z. L.、Choi, T. M.、Au, K. F.、Yu, Y.(2008)。Sales forecasting using extreme learning machine with applications in fashion retailing。Decision Support Systems,46(1),411-419。  new window
3.Chen, F. L.、Ou, T. Y.(2011)。Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry。Expert Systems with Applications,38(3),1336-1345。  new window
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6.Choi, T. M.、Hui, C. L.、Liu, N.、Ng, S. F.、Yu, Y.(2014)。Fast fashion sales forecasting with limited data and time。Decision Support Systems,59,84-92。  new window
7.錢炳全、李順益、王學亮(20030300)。基於灰色理論之短期銷售預測方法。資訊管理展望,5(1),1-17。  延伸查詢new window
8.Chang, P. C.、Liu, C. H.、Fan, C. Y.(2009)。Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry。Knowledge-Based Systems,22(5),344-355。  new window
9.Kalekar, P. S.(2004)。Time series forecasting using holt-winters exponential smoothing。Kanwal Rekhi School of Information Technology,4329008,1-13。  new window
10.Kim, S.、Kim, H.(2016)。A new metric of absolute percentage error for intermittent demand forecasts。International Journal of Forecasting,32(3),669-679。  new window
11.Koochakpour, K.、Tarokh, M. J.(2016)。Sales budget forecasting and revision by adaptive network fuzzy base inference system and optimization methods。Journal of Computer & Robotics,9(1),25-38。  new window
12.Lei, M.、Feng, Z.(2012)。A proposed grey model for short-term electricity price forecasting in competitive power markets。International Journal of Electrical Power & Energy Systems,43(1),531-538。  new window
13.Palia, A. P.、Roussos, D. S.(2004)。Online sales forecasting with the multiple regression analysis data matrices package。Developments in Business Simulation and Experiential Learning,31,53-57。  new window
14.Yang, L.、Li, B.(2016)。The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network。International Journal of Database Theory and Application,9(1),67-76。  new window
15.Wang, Z. X.、Ye, D. J.(2017)。Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models。Journal of Cleaner Production,142(2),600-612。  new window
16.Crawford, Gordon W.、Fratantoni, Michael C.(2003)。Assessing the forecasting performance of regime-switching, ARIMA and GARCH models of house prices。Real Estate Economics,31(2),223-243。  new window
會議論文
1.Chen, Y.、Liu, P.、Yu, L.(2010)。Aftermarket demands forecasting with a Regression-Bayesian-BPNN model。2010 International Conference on Intelligent Systems and Knowledge Engineering。IEEE。52-55。  new window
2.Qin, Y.、Yun, C.(2012)。Auto parts demand forecasting based on nonnegative variable weight combination model in auto aftermarket。2012 International Conference on Management Science and Engineering。IEEE。817-822。  new window
圖書
1.DeLurgio, S. A.(1998)。Forecasting principles and applications。New York:Irwin/McGraw-Hill。  new window
2.Pillai, R. S. N.(2010)。Marketing Management。S. Chand Publishing。  new window
圖書論文
1.許麗芩(2011)。年貨大街深度巡禮六--年貨百年風華。百年迪化風華。策馬入林文化事業有限公司。  延伸查詢new window
 
 
 
 
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