在現代商業環境中，銷售數據分析對於提升銷售績效具有重要意義。同時，應對商業環境的複雜性和快速變化也至關重要。然而，傳統的異常偵測方法主要依賴歷史數據和基本的統計分析技術，難以及時發現和解釋銷售異常。
本研究提出一種基於注意力機制與深度學習預測模型的銷售異常原因分析方法。該方法透過銷售預測來偵測銷售異常，再運用注意力機制分析銷售異常原因。本方法藉由動態調整銷售影響因素的權重，提高了原因分析的準確度，能幫助企業更有效地識別銷售異常的原因，從而提供決策支援。
本研究首先設計所提之銷售異常原因分析方法，再開發本方法之實現技術，包括：資料處理技術、銷售預測技術與原因分析技術等技術，最終進行本方法之應用有效評量。
本研究的主要貢獻在於結合注意力機制和深度學習預測模型，開發了一種創新的銷售異常原因分析方法。透過Y公司的銷售數據，驗證了該方法的正確性。結果顯示使用注意力機制後的模型預測準確度平均提升了約4%至5%。此外，本研究經由實驗得知深度學習模型的預測效能普遍優於傳統統計方法。根據這些結果，我們最終選擇效果最好的深度學習模型，以提升銷售異常原因分析的準確性與效能。

In the modern business environment, sales data analysis is essential for improving performance and managing complexity. Traditional anomaly detection methods often fail to promptly identify and interpret sales anomalies.
This study introduces a method for analyzing sales anomalies using an attention mechanism and deep learning models. By dynamically adjusting the weights of influencing factors, this method enhances cause analysis accuracy, helping businesses identify and address sales anomalies effectively.
The proposed approach involves analyzing factors such as social media comments, events, dates, weather, and holidays, followed by designing and applying deep learning models, including LSTM, Multi-LSTM, and CNN-LSTM. Anomalies in the predicted sales data are then detected, and an attention mechanism is used to determine key influencing factors by dynamically adjusting their weights.
The primary contribution of this study is the employment of the attention mechanism with deep learning models, resulting in an innovative approach to sales anomaly analytics. Validated with Company Y's sales data(20200601-20220531), this method not only detects anomalies but also provides detailed cause analysis, supporting better decision-making and enhancing market responsiveness.

中文文獻
林柏呈.（2023）. 基於機器學習之多步銷售預測方法研究. 2023. PhD Thesis.
徐浩翔.（2022）. 基於重要影響因素之銷售預測方法與技術開發. PhD Thesis.
郭家蓁.（2023）. 新顧客關係管理模式之顧客價值預測方法與技術開發. 2023. PhD Thesis.
英文文獻
Agnihotri, R., Dingus, R., Hu, M. Y., & Krush, M. T. (2016). Social Media: Influencing Customer Satisfaction in B2B Sales. Industrial Marketing Management, 53, 172-180.
Aladangady, A., Aron-Dine, S., Dunn, W., Feiveson, L., Lengermann, P., & Sahm, C. (2017). The Effect of Sales-Tax Holidays on Consumer Spending. , 2017.
Alvarez, F. M., Troncoso, A., Riquelme, J. C., & Ruiz, J. S. A. (2010). Energy Time Series Forecasting Based on Pattern Sequence Similarity. IEEE Transactions on Knowledge and Data Engineering, 23(8), 1230-1243.
Badorf, F., & Hoberg, K. (2020). The Impact of Daily Weather on Retail Sales: An Empirical Study in Brick-and-Mortar Stores. Journal of Retailing and Consumer Services, 52, 101921.
Berberich, D. (2021). Hybrid Methods for Time Series Forecasting (Doctoral dissertation, Karlsruher Institut für Technologie (KIT)).
Biswas, B., Sanyal, M. K., & Mukherjee, T. (2023). AI-Based Sales Forecasting Model for Digital Marketing. International Journal of E-Business Research (IJEBR), 19(1), 1-14.
Borsellino, V., Kaliji, S., & Schimmenti, E. (2020). COVID-19 Drives Consumer Behaviour and Agro-Food Markets towards Healthier and More Sustainable Patterns. Sustainability.
Bozbura, M., Tunç, H. C., Kusak, M. E., & Sakar, C. O. (2019, January). Detection of E-Commerce Anomalies Using LSTM-Recurrent Neural Networks. In DATA (pp. 217-224).
Brauwers, G., & Frasincar, F. (2022). A General Survey on Attention Mechanisms in Deep Learning. IEEE Transactions on Knowledge and Data Engineering, 35, 3279-3298.
Böhmer, K., & Rinderle-Ma, S. (2016). Multi-Perspective Anomaly Detection in Business Process Execution Events. In On the Move to Meaningful Internet Systems: OTM 2016 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2016, Rhodes, Greece, October 24-28, 2016, Proceedings (pp. 80-98). Springer International Publishing.
Cader, J. (2017). From (Anti-Mainland) Sinophobia and Shibboleths to Mobilisation on a Taiwanese Message Board. , 163-174.
Chen, W., & Shi, K. (2021). Multi-Scale Attention Convolutional Neural Network for Time Series Classification. Neural Networks: The Official Journal of the International Neural Network Society, 136, 126-140.
Davidow, M., & Matteson, D. S. (2022). Factor Analysis of Mixed Data for Anomaly Detection. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 480-493.
Dolega, L., Rowe, F., & Branagan, E. (2021). Going Digital？ The Impact of Social Media Marketing on Retail Website Traffic, Orders and Sales. Journal of Retailing and Consumer Services, 60, 102501.
Ekbote, B. (2017). Dynamic Sales Forecasting Method for Increased Accuracy. Econometrics: Econometric & Statistical Methods - Special Topics eJournal.
Fang, F., Ventre, C., Li, L., Kanthan, L., Wu, F., & Basios, M. (2020). Better Model Selection with a New Definition of Feature Importance. arXiv preprint arXiv:2009.07708.
Farias, V. F., Li, A. A., & Peng, T. (2020). Solving the Phantom Inventory Problem: Near-Optimal Entry-Wise Anomaly Detection. stat, 1050, 23.
Feng, P. (2004). Anomaly Detection Techniques Based on Statistics. Journal of Shanghai Jiaotong University.
Frees, E., & Miller, T. (2004). Sales Forecasting Using Longitudinal Data Models. International Journal of Forecasting, 20, 99-114.
Gao, Y., Su, P., Zhao, H., Qiu, M., & Liu, M. (2021). Research on Sentiment Dictionary Based on Sentiment Analysis in News Domain. 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 117-122.
Gardner Jr, E. S., & McKenzie, E. D. (1985). Forecasting Trends in Time Series. Management Science, 31(10), 1237-1246.
Gow, R., Rabhi, F. A., & Venugopal, S. (2017). Anomaly Detection in Complex Real World Application Systems. IEEE Transactions on Network and Service Management, 15(1), 83-96.
Herrmann, C., & Knight, R. (2001). Mechanisms of Human Attention: Event-Related Potentials and Oscillations. Neuroscience & Biobehavioral Reviews, 25, 465-476.
Hill, T., O'Connor, M., & Remus, W. (1996). Neural Network Models for Time Series Forecasts. Management Science, 42(7), 1082-1092.
Hirche, M., Haensch, J., & Lockshin, L. (2021). Comparing the Day Temperature and Holiday Effects on Retail Sales of Alcoholic Beverages–A Time-Series Analysis. International Journal of Wine Business Research, 33(3), 432-455.
Hrytsyshyn, A. (2020). Accounting for Sales Activities in the Context of the Separation of Logistics and Marketing Processes. Herald of Economics, (1 (95)), 200-212.
Isa, N., Yusof, N. S., & Ramlan, M. A. (2019). The Implementation of Data Mining Techniques for Sales Analysis Using Daily Sales Data. International Journal of Advanced Trends in Computer Science and Engineering, 8(1.5), 74-80.
Jabrouni, H., Kamsu-Foguem, B., Geneste, L., & Vaysse, C. (2011). Continuous improvement through knowledge-guided analysis in experience feedback. Eng. Appl. Artif. Intell., 24, 1419-1431.
Jiang, K., Huang, Z., Zhou, X., Tong, C., Zhu, M., & Wang, H. (2023). Deep belief improved bidirectional LSTM for multivariate time series forecasting.. Mathematical biosciences and engineering : MBE, 20 9, 16596-16627 .
Jiang, Z., Wu, Z., Dai, Y., & Sun, H. (2021). Research on Product Sales Forecast Based on Online Search Behavior. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 5, 2425-2429.
Jin, X., Yu, X., Wang, X., Bai, Y., Su, T., & Kong, J. (2019). Prediction for Time Series with CNN and LSTM. , 631-641.
Kalli, N. S., & Pullagura, H. T. (2020). Predicting Total Business Sales Using Time Series Analysis.
Kardakis, S., Perikos, I., Grivokostopoulou, F., & Hatzilygeroudis, I. (2021). Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis. Applied Sciences, 11, 3883.
Kaushal, R. K., Giri, K. K., Kumar, H. S., Raina, K., Choudhury, D., & Naikade, K. (2023, October). AI-Based Approach for Retail Sale Forecasting. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 111-116). IEEE.
Khurana, U., Samulowitz, H., & Turaga, D. (2018, April). Feature Engineering for Predictive Modeling Using Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
Ko, T., Lee, J., Cho, H., Cho, S., Lee, W., & Lee, M. (2017). Machine Learning-Based Anomaly Detection via Integration of Manufacturing, Inspection and After-Sales Service Data. Ind. Manag. Data Syst., 117, 927-945.
Ko, J., & Comuzzi, M. (2021). Detecting Anomalies in Business Process Event Logs Using Statistical Leverage. Inf. Sci., 549, 53-67.
Lee, K. S., Lee, S. B., & You, Y. Y. (2016). A Study on the Influence Factor of Traditional Marketplace Sales. Indian Journal of Science and Technology.
Lin, J., & Liu, T. (2003). Modeling Lunar Calendar Holiday Effects in Taiwan. , 33, 1-37.
Liu, L., Lei, M., & Wang, H. (2013). Combining Domain-Specific Sentiment Lexicon with Hownet for Chinese Sentiment Analysis. J. Comput., 8, 878-883.
Liu, S., & Chen, J. (2015). A Multi-Label Classification Based Approach for Sentiment Classification. Expert Syst. Appl., 42, 1083-1093.
Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C., & Karypis, G. (2021). Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning. IEEE Transactions on Neural Networks and Learning Systems, 33, 2378-2392.
MARQUES TAVARES, G., da Costa, V. T., Martins, V., Ceravolo, P., & Sylvio Barbon, J. (2019). Leveraging Anomaly Detection in Business Process with Data Stream Mining. ISYS, 12(1), 54-75.
Ma, T., Rong, H., Hao, Y., Cao, J., Tian, Y., & Al-Rodhaan, M. (2019). A Novel Sentiment Polarity Detection Framework for Chinese. IEEE Transactions on Affective Computing, 13, 60-74.
Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q., & Xiong, H. (2021). A Comprehensive Survey on Graph Anomaly Detection with Deep Learning. IEEE Transactions on Knowledge and Data Engineering, 35, 12012-12038.
Madhani, P. M. (2017). Logistics and Marketing Integration: Enhancing Competitive Advantages. The IUP Journal of Management Research, 16(3), 7-29.
Mao, S., & Xiao, F. (2019). Time Series Forecasting Based on Complex Network Analysis. IEEE Access, 7, 40220-40229.
Martynova, O. V., & Akhmetshin, R. M. (2023). Description of Business Processes of the Sales Activities of an Industrial Enterprise. Entrepreneur’s Guide, 16(4).
McGranahan, L., & Marwell, N. (2010). The Effect of Sales Tax Holidays on Household Consumption Patterns.
Merigo, J. M., Palacios-Marques, D., & Ribeiro-Navarrete, B. (2015). Aggregation Systems for Sales Forecasting. Journal of Business Research, 68(11), 2299-2304.
Minfei, W., Shiyuan, T., Qi, P., Jianmin, G., Ying, L., & Jian, S. (2023). Sales Forecasting Model Based on Ensemble Learning and Its Application in Anomaly Detection. Proceedings of the 2023 7th International Conference on Digital Signal Processing.
Mishra, S., & Panda, M. (2021). Applications of Feature Engineering Techniques for Text Data. In Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (pp. 182-194). IGI Global.
Munir, M., Siddiqui, S., Dengel, A., & Ahmed, S. (2019). DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series. IEEE Access, 7, 1991-2005.
Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. S. (2017, August). Learning Feature Engineering for Classification. In Ijcai (Vol. 17, pp. 2529-2535).
Ngongo, N. C. K., & Darteh, O. F. (2022). Sales Anomaly Detection Using Automatic Time Series Decomposition. Journal of Economics, Management and Trade, 28(9), 13-21.
Nikose, M., Mungale, T., Shelke, M., Shelote, R., & Solanke, P. (2022). Best Selling Product and Category Prediction Using Sales Analysis. International Journal of Advanced Research in Science, Communication and Technology.
Niu, Z., Zhong, G., & Yu, H. (2021). A Review on the Attention Mechanism of Deep Learning. Neurocomputing, 452, 48-62.
Nolle, T., Luettgen, S., Seeliger, A., & Mühlhäuser, M. (2018). Analyzing Business Process Anomalies Using Autoencoders. Machine Learning, 107, 1875-1893.
Oyamada, M. (2019, December). Extracting Feature Engineering Knowledge from Data Science Notebooks. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 6172-6173). IEEE.
Pang, G., Shen, C., & Hengel, A. (2019). Deep Anomaly Detection with Deviation Networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
Pataropura, A., Riki, R., & Saputra, A. (2019). Sales Analysis Using the Forecasting Method. bit-Tech: Binary Digital-Technology, 1(3), 144-147.
Pham, T., & Lee, S. (2016). Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods. ArXiv, abs/1611.03941.
Pivette, M., Mueller, J., Crépey, P., & Bar-Hen, A. (2014). Drug Sales Data Analysis for Outbreak Detection of Infectious Diseases: A Systematic Literature Review. BMC Infectious Diseases, 14.
Posner, M. (1982). Cumulative Development of Attentional Theory. American Psychologist, 37, 168-179.
Prabhakaran, S. (2019). Forecasting an Explosive Time Series. Electronic Journal of Applied Statistical Analysis, 12(3), 674-690.
Pustokhina, I. V., & Pustokhin, D. A. (2023). A Comparative Analysis of Traditional Forecasting Methods and Machine Learning Techniques for Sales Prediction in E-Commerce. American Journal of Business and Operations Research, 10(2), 39-9.
Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. (2017). A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. arXiv preprint arXiv:1704.02971.
Ranjan, S., & Mishra, S. (2021). Perceiving University Students' Opinions from Google App Reviews. Concurrency and Computation: Practice and Experience, 34.
Rose, N., & Dolega, L. (2021). It’s the Weather: Quantifying the Impact of Weather on Retail Sales. Applied Spatial Analysis and Policy, 15, 189-214.
S, S., C, A., S, C., C, C., & L, A. (2023). Big Mart Sales Prediction Using Machine Learning. IJARCCE.
Saboo, A. R., Kumar, V., & Ramani, G. (2016). Evaluating the Impact of Social Media Activities on Human Brand Sales. International Journal of Research in Marketing, 33(3), 524-541.
Saknure, V. B., & Jadhav, D. V. Data Analysis in Business Intelligence for Mathematical Prediction of Online Sale.
Sayyida, S., Hartini, S., Gunawan, S., & Husin, S. (2021). The Impact of the COVID-19 Pandemic on Retail Consumer Behavior. Aptisi Transactions on Management (ATM).
Schmidl, S., Wenig, P., & Papenbrock, T. (2022). Anomaly Detection in Time Series: A Comprehensive Evaluation. Proceedings of the VLDB Endowment, 15(9), 1779-1797.
Sharma, S., & Sharma, V. (2012). Comparative Analysis of Machine Learning Techniques in Sale Forecasting. International Journal of Computer Applications, 53, 51-54.
Shi, H., Ma, Z., Chong, D., & He, W. (2019). Impact of Social Media on Real Estate Sales. In The Ecosystem of e-Business: Technologies, Stakeholders, and Connections: 17th Workshop on e-Business, WeB 2018, Santa Clara, CA, USA, December 12, 2018, Revised Selected Papers 17 (pp. 3-14). Springer International Publishing.
Simchi-Levi, D., Sun, R., Wu, M., & Zhu, R. (2023). Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression. Management Science.
Sinitsyn, M. (2017). Pricing with Prescheduled Sales. Mark. Sci., 36, 999-1014.
Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2020). Export Sales Forecasting Using Artificial Intelligence. Technological Forecasting and Social Change.
Sun, Z., Choi, T., Au, K., & Yu, Y. (2008). Sales Forecasting Using Extreme Learning Machine with Applications in Fashion Retailing. Decis. Support Syst., 46, 411-419.
Sun, Y., Fang, M., Wang, X., & Diao, S. (2017). GubaLex: Guba-Oriented Sentiment Lexicon for Big Texts in Finance. 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), 25-32.
Taha, A., & Hadi, A. S. (2019). Anomaly Detection Methods for Categorical Data: A Review. ACM Computing Surveys (CSUR), 52(2), 1-35.
Toshniwal, A., Mahesh, K., & Jayashree, R. (2020, October). Overview of Anomaly Detection Techniques in Machine Learning. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 808-815). IEEE.
Verdonck, T., Baesens, B., Óskarsdóttir, M., & vanden Broucke, S. (2021). Special Issue on Feature Engineering Editorial. Machine Learning, 1-12.
Wardati, N. K., & Mahendrawathi, E. R. (2019). The Impact of Social Media Usage on the Sales Process in Small and Medium Enterprises (SMEs): A Systematic Literature Review. Procedia Computer Science, 161, 976-983.
Warnick, R., Bojanic, D., & Xu, F. (2015). Using a Trade Market Analysis Technique to Refine Measurements for Economic Impact Analysis of Special Events. Journal of Travel Research, 54, 52 - 65.
Widiputra, H., Mailangkay, A., & Gautama, E. (2021). Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction. Complex., 2021, 9903518:1-9903518:14.
Winters, P. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6, 324-342.
Wong, F. S. (1991). Time Series Forecasting Using Backpropagation Neural Networks. Neurocomputing, 2(4), 147-159.
Wu, S. F., Chang, C. Y., & Lee, S. J. (2015, March). Time Series Forecasting with Missing Values. In 2015 1st International Conference on Industrial Networks and Intelligent Systems (INISCom) (pp. 151-156). IEEE.
Xiang, Z., Zou, Y., & Wang, X. (2014). Sentiment Analysis of Chinese Micro-Blog Using Vector Space Model. Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, 1-5.
Xing, L., Yuan, L., Wang, Q., & Yu, L. (2015). An Approach to Sentiment Analysis of Short Chinese Texts Based on SVMs. 2015 34th Chinese Control Conference (CCC), 9115-9120.
Xu, B., & Zhang, Y. (2010). A New Polarity Clustering Algorithm Based on Semantic Criterion Function for Text of the Chinese Commentary. 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 4, V4-116-V4-119.
Xu, Z., Liao, Y., & Zhan, S. (2022). Xlnet Parallel Hybrid Network Sentiment Analysis Based on Sentiment Word Augmentation. 2022 7th International Conference on Computer and Communication Systems (ICCCS), 63-68.
Zhang, J., Ren, M., Xiao, X., & Zhang, J. (2017). Providing Consumers with a Representative Subset from Online Reviews. Online Inf. Rev., 41, 877-899.
Zhang, C., Cao, L., & Romagnoli, A. (2018). On the Feature Engineering of Building Energy Data Mining. Sustainable Cities and Society, 39, 508-518.
Zhang, C., Tian, Y., & Fan, Z. (2021). Forecasting Sales Using Online Review and Search Engine Data: A Method Based on PCA–DSFOA–BPNN. International Journal of Forecasting.
Zhang, C., Tian, Y. X., & Fan, Z. P. (2022). Forecasting Sales Using Online Review and Search Engine Data: A Method Based on PCA–DSFOA–BPNN. International Journal of Forecasting, 38(3), 1005-1024.
Štreimikienė, D., Mikalauskienė, A., Sturienė, U., & Kyriakopoulos, G. L. (2021). The Impact of Social Media on Sales Promotion in Entertainment Companies.
Сарай, Н. І. (2021). Оптимізація Економічного Аналізу Асортиментної Політики в Контексті Стратегічних Цілей Торговельного Підприємства.