頁籤選單縮合
題 名 | Forecast Seasonal Time Series by Comparing Five Kinds of Hybrid Grey Models |
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作 者 | Tseng,Fang-mei; Tzeng,Gwo-hsiung; | 書刊名 | 模糊系統學刊 |
卷 期 | 5:2 1999.12[民88.12] |
頁 次 | 頁45-55 |
分類號 | 440.8 |
關鍵詞 | Back-propagation; Grey forecasting; Neural network; Possibility grey model; Time series; |
語 文 | 英文(English) |
英文摘要 | The grey forecasting model has been successfully applied to finance, physical control, engineering, economics, etc. However, the seasonal time series forecast seldom tested. The authors applied and compared the five model to forecast time series with seasonality characteristics, i.e., the GM(1,1) grey forecasting model combined with the ratio-to-moving-average deseasonalization method, the possibility grey model, the neural network back-propagation model combined with grey relation, the GM(1,1) grey model with raw data and the GM(1,N) grey model combined with grey relation. Three criteria, i.e., the mean squares error (MSE), the mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the performance of the four model, except the possibility grey model. The time series data of the total production value of Taiwan's machinery industry (January 1994 to December 1997) and the sales volume of soft drinks reported from Montgomery's book were used as test data sets. The MSE, the MAE and the MAPE of the GM(1,1) combined with the ratio-to-moving average model were the lowest. The possibility grey model didn't provide good information for the future values. |
本系統中英文摘要資訊取自各篇刊載內容。