網路搜尋量及月營收歷史資料預測未來月營收—機器學習與深度學習之應用

Abstract

本論文旨在探討機器學習和深度學習模型在月營收預測中的應用，並進一步考慮網路搜尋量變數和季節性因素對預測準確度的影響。研究結果表明，有效的模型設計可以解決基礎模型在反映實際情況上的落後性問題，提高預測準確度。同時，考慮長時間段的月營收資料能夠更好地捕捉趨勢並避免異常值的影響。此外，資料的平減處理和網路搜尋量變數的加入均能提高預測準確度，尤其是在難以預測的時間段。 在模型比較方面，根據不同的情境，機器學習模型和深度學習模型的表現存在差異。當考慮網路搜尋量變數和平減後的原始資料時，機器學習模型的表現較好；而當考慮網路搜尋量變數和平減後的分解資料時，深度學習模型相對於機器學習模型表現較好。進一步的變數重要性分析表明，不同模型對趨勢、季節性和殘差的影響有所不同，這有助於選擇合適的機器學習模型以提高預測準確度。最後，季節性對月營收的影響被證明是重要的，忽略季節性將導致預測結果明顯較差。 本論文的結果為業界和學術界提供了有價值的洞察和實用工具，可用於改進月營收的預測準確度。透過機器學習和深度學習模型，考慮網路搜尋量變數和季節性因素，能夠更準確地預測月營收並更好地理解其變化趨勢。這將有助於投資人做出更明智的投資決策，提高投資效益並降低風險。未來的研究可進一步探索其他影響月營收的因素，並開發更精確的預測模型，以應對不斷變化的商業環境。

The aim of this study is to explore the application of machine learning and deep learning models in monthly revenue forecasting, while considering the impact of search volume variables and seasonal factors on prediction accuracy. The results show that effective model design can address the lagging issue of baseline models in reflecting real-world situations and improve prediction accuracy. Additionally, considering long time periods of monthly revenue data can better capture trends and avoid the influence of outliers. Furthermore, data deflating and the inclusion of search volume variables both contribute to improved prediction accuracy, especially during difficult-to-predict time periods. In terms of model comparison, the performance of machine learning models and deep learning models differs depending on the context. Machine learning models perform better when considering search volume variables and deflating raw data, while deep learning models outperform machine learning models when considering search volume variables and deflating decomposed data. Further analysis of variable importance reveals that different models have varying impacts on trends, seasonality, and residuals, which helps in selecting the appropriate machine learning model to enhance prediction accuracy. Finally, the influence of seasonality on monthly revenue is proven to be significant, and neglecting seasonality leads to significantly poorer prediction results. The results of this study provide valuable insights and practical tools for industry and academia to improve the accuracy of monthly revenue forecasting. Through the use of machine learning and deep learning models, considering search volume variables and seasonal factors, more accurate predictions of monthly revenue can be achieved, and a better understanding of its changing trends can be obtained. This will aid investors in making more informed investment decisions, increasing investment returns, and reducing risks. Future research can further explore other factors that impact monthly revenue and develop more precise prediction models to adapt to the ever-changing business environment.