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    题名: 提高銷售預測準確性：判斷式、Prophet及混合預測方法之比較分析
    Enhancing sales forecasting accuracy: a comparative analysis of judgmental, prophet, and hybrid forecasting approaches
    作者: 卡西歐
    Finotti, Cassio C.
    贡献者: 莊皓鈞
    Chuang, Howard
    卡西歐
    Cassio C. Finotti
    关键词: 預測
    預測模型
    判斷性預測
    銷售數據
    時間序列分析
    Forecasting
    Prophet model
    Judgmental forecasts
    Sales data
    Time-series analysis
    日期: 2023
    上传时间: 2023-07-06 16:34:12 (UTC+8)
    摘要: .
    This thesis explores how Prophet, a time-series model by Meta, can enhance judgmental forecasts for predicting the monthly demand of ten products from a single customer of a B2B manufacturing company. The dataset spans from 2018-2022, providing monthly sales data with 2022 as the focus. Forecast accuracy is assessed using the Cumulative Forecast Error (CFE) method. Results show that the Prophet model excels judgmental forecasts in 6 out of 10 products, and a Hybrid approach of incorporating judgmental forecasts as regressors improve performance, outperforming in 8 out of 10 products. The findings show the benefits of integrating advanced statistical models like Prophet into business forecasting processes to mitigate over and underforecasting and boost accuracy. The study outlines limitations and future research opportunities, such as expanding datasets, exploring new comparison metrics, and periodically updating the Prophet model. Practical implications discuss challenges and benefits of statistical forecasting models, Prophet’s accessibility, and the need to counter underforecasting and overforecasting. By harnessing new technologies, businesses can enhance operations and improve demand forecasting accuracy. This thesis highlights the potential of merging statistical models like Prophet with judgmental forecasts and proposes areas for further exploration to refine these models’ effectiveness in business contexts.
    參考文獻: Reference
    1. Chen, H., Frank, M. Z., & Wu, O. Q. (2005). What Actually Happened to the Inventories of American Companies Between 1981 and 2000? Management Science, 51(7), 1015-1031.
    2. De Livera, A., Hyndman, R. J., & Snyder, R. (2010). Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106, 1513-1527.
    3. Facebook. (n.d.). Quick start — Prophet 1.0 documentation. Retrieved March 31, 2023, from https://facebook.github.io/prophet/docs/quick_start.html
    4. Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576. http://www.jstor.org/stable/20141547
    5. Goodwin, P. (2002). Integrating management judgment and statistical methods to improve short-term forecasts. Omega, 30(2), 127-135.
    6. Helmer, O. (1983). Looking forward: a guide to future research. Sage Publications, Inc.
    7. Hsu, C. C., & Sandford, B. A. (2007). The Delphi technique: making sense of consensus. Practical Assessment, Research, and Evaluation, 12(1), 10.
    8. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
    9. Kutzkov, K. (2023, April 19). ARIMA vs Prophet vs LSTM for Time Series Prediction [web log]. Retrieved April 22, 2023, from https://neptune.ai/blog/arima-vs-prophet-vs-lstm.
    10. Lawrence, M., Goodwin, P., O`Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3).
    11. Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices. Forecasting, 3, 644–662.
    12. Moon, M. A., Mentzer, J. T., & Smith, C. D. (2003). Researching Sales Forecasting Practice: Commentaries and authors` response on "Conducting a Sales Forecasting Audit." International Journal of Forecasting, 19, 27-42.
    13. Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: issues and analysis. International Journal of Forecasting, 15(4), 353-375.
    14. Smolic, H. (2022, September 1). Sales Forecasting: How to Apply Machine Learning. Graphite Note. Retrieved May 10, 2023, from https://graphite-note.com/machine-learning-sales-forecasting
    15. Taylor, S. J., & Letham, B. (2017). Forecasting at Scale. The American Statistician, 72(1).
    16. Van der Heijden, K. (2005). Scenarios: The Art of Strategic Conversation. John Wiley & Sons.
    17. Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53, 5929–5955.
    18. Wright, G., & Goodwin, P. (2009). Decision making and planning under low levels of predictability: Enhancing the scenario method. International Journal of Forecasting, 25(4), 813-825.
    描述: 碩士
    國立政治大學
    國際經營管理英語碩士學位學程(IMBA)
    110933046
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110933046
    数据类型: thesis
    显示于类别:[國際經營管理英語碩士學程IMBA] 學位論文

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