ARIMA model analyzes the tendency and challenges of intelligent marketing in the era of digitalization

[1] Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of Big Data Analytics in European firms. Journal of Business Research, 70, 379-390. Côrte-RealN.OliveiraT.RuivoP. (2017). Assessing business value of Big Data Analytics in European firms. Journal of Business Research, 70, 379-390. Search in Google Scholar

[2] Popovic, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20, 209-222. PopovicA.HackneyR.TassabehjiR.CastelliM. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20, 209-222. Search in Google Scholar

[3] Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423. GroverV.ChiangR. H.LiangT. P.ZhangD. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423. Search in Google Scholar

[4] Müller, S. D., & Jensen, P. (2017). Big data in the Danish industry: application and value creation. Business Process Management Journal, 23(3), 645-670. MüllerS. D.JensenP. (2017). Big data in the Danish industry: application and value creation. Business Process Management Journal, 23(3), 645-670. Search in Google Scholar

[5] Vitari, C., & Raguseo, E. (2020). Big data analytics business value and firm performance: linking with environmental context. International Journal of Production Research, 58(18), 5456-5476. VitariC.RaguseoE. (2020). Big data analytics business value and firm performance: linking with environmental context. International Journal of Production Research, 58(18), 5456-5476. Search in Google Scholar

[6] Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. AmadoA.CortezP.RitaP.MoroS. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. Search in Google Scholar

[7] Cao, G., Tian, N., & Blankson, C. (2022). Big data, marketing analytics, and firm marketing capabilities. Journal of Computer Information Systems, 62(3), 442-451. CaoG.TianN.BlanksonC. (2022). Big data, marketing analytics, and firm marketing capabilities. Journal of Computer Information Systems, 62(3), 442-451. Search in Google Scholar

[8] Ducange, P., Pecori, R., & Mezzina, P. (2018). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing, 22(1), 325-342. DucangeP.PecoriR.MezzinaP. (2018). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing, 22(1), 325-342. Search in Google Scholar

[9] Yau, K. L. A., Saad, N. M., & Chong, Y. W. (2021). Artificial intelligence marketing (AIM) for enhancing customer relationships. Applied Sciences, 11(18), 8562. YauK. L. A.SaadN. M.ChongY. W. (2021). Artificial intelligence marketing (AIM) for enhancing customer relationships. Applied Sciences, 11(18), 8562. Search in Google Scholar

[10] Liu, X. Y. (2021). Agricultural products intelligent marketing technology innovation in big data era. Procedia Computer Science, 183, 648-654. LiuX. Y. (2021). Agricultural products intelligent marketing technology innovation in big data era. Procedia Computer Science, 183, 648-654. Search in Google Scholar

[11] Liu, H., & Zhao, H. (2022). Upgrading models, evolutionary mechanisms and vertical cases of service-oriented manufacturing in SVC leading enterprises: Product-development and service-innovation for industry 4.0. Humanities and Social Sciences Communications, 9(1), 1-24. LiuH.ZhaoH. (2022). Upgrading models, evolutionary mechanisms and vertical cases of service-oriented manufacturing in SVC leading enterprises: Product-development and service-innovation for industry 4.0. Humanities and Social Sciences Communications, 9(1), 1-24. Search in Google Scholar

[12] Pereira, R. M., Borini, F. M., & Oliveira Jr, M. D. M. (2023). Technological Upgrade in Emerging Markets: The Importance of Interorganizational Cooperation for Product and Process Upgrades. International Journal of Innovation and Technology Management, 20(08), 2350051. PereiraR. M.BoriniF. M.Oliveira JrM. D. M. (2023). Technological Upgrade in Emerging Markets: The Importance of Interorganizational Cooperation for Product and Process Upgrades. International Journal of Innovation and Technology Management, 20(08), 2350051. Search in Google Scholar

[13] Wiegand, N., & Imschloss, M. (2021). Do You Like What You (Can’t) See? The Differential Effects of Hardware and Software Upgrades on High-Tech Product Evaluations. Journal of Interactive Marketing, 56(1), 18-40. WiegandN.ImschlossM. (2021). Do You Like What You (Can’t) See? The Differential Effects of Hardware and Software Upgrades on High-Tech Product Evaluations. Journal of Interactive Marketing, 56(1), 18-40. Search in Google Scholar

[14] Nuruzzaman, N., & Singh, D. (2019). Exchange characteristics, capability upgrading and innovation performance: evidence from Latin America. Journal of Knowledge Management, 23(9), 1747-1763. NuruzzamanN.SinghD. (2019). Exchange characteristics, capability upgrading and innovation performance: evidence from Latin America. Journal of Knowledge Management, 23(9), 1747-1763. Search in Google Scholar

[15] Singh, R. K., Rani, M., Bhagavathula, A. S., Sah, R., Rodriguez-Morales, A. J., Kalita, H., ... & Kumar, P. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance, 6(2), e19115. SinghR. K.RaniM.BhagavathulaA. S.SahR.Rodriguez-MoralesA. J.KalitaH.KumarP. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance, 6(2), e19115. Search in Google Scholar

[16] Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., & Saia, R. (2018). Forecasting e-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and google trends data. Future Internet, 11(1), 5. CartaS.MeddaA.PiliA.Reforgiato RecuperoD.SaiaR. (2018). Forecasting e-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and google trends data. Future Internet, 11(1), 5. Search in Google Scholar

[17] Abu Bakar, N., & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130-137. Abu BakarN.RosbiS. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130-137. Search in Google Scholar

[18] Bakar, N. A., & Rosbi, S. (2017). Data clustering using Autoregressive Integrated Moving Average (ARIMA) model for Islamic country currency: an econometrics method for Islamic financial engineering. The International Journal of Engineering and Science (IJES), 6(6), 22-31. BakarN. A.RosbiS. (2017). Data clustering using Autoregressive Integrated Moving Average (ARIMA) model for Islamic country currency: an econometrics method for Islamic financial engineering. The International Journal of Engineering and Science (IJES), 6(6), 22-31. Search in Google Scholar

[19] Moon, S., Chi, S., & Kim, D. Y. (2018). Predicting construction cost index using the autoregressive fractionally integrated moving average model. Journal of Management in Engineering, 34(2), 04017063. MoonS.ChiS.KimD. Y. (2018). Predicting construction cost index using the autoregressive fractionally integrated moving average model. Journal of Management in Engineering, 34(2), 04017063. Search in Google Scholar

[20] Nguyen, H. V., Naeem, M. A., Wichitaksorn, N., & Pears, R. (2019). A smart system for short-term price prediction using time series models. Computers & Electrical Engineering, 76, 339-352. NguyenH. V.NaeemM. A.WichitaksornN.PearsR. (2019). A smart system for short-term price prediction using time series models. Computers & Electrical Engineering, 76, 339-352. Search in Google Scholar

[21] Sharma, A., Patel, N., & Gupta, R. (2021). Enhancing Retail Sales Forecasting through LSTM Networks and ARIMA Models: A Comparative Analysis of AI Methodologies. European Advanced AI Journal, 10(2). SharmaA.PatelN.GuptaR. (2021). Enhancing Retail Sales Forecasting through LSTM Networks and ARIMA Models: A Comparative Analysis of AI Methodologies. European Advanced AI Journal, 10(2). Search in Google Scholar

[22] Ping, H., Li, Z., Shen, X., & Sun, H. (2024). Optimization of vegetable restocking and pricing strategies for innovating supermarket operations utilizing a combination of ARIMA, LSTM, and FP-Growth algorithms. Mathematics, 12(7), 1054. PingH.LiZ.ShenX.SunH. (2024). Optimization of vegetable restocking and pricing strategies for innovating supermarket operations utilizing a combination of ARIMA, LSTM, and FP-Growth algorithms. Mathematics, 12(7), 1054. Search in Google Scholar

[23] Van Calster, T., Baesens, B., & Lemahieu, W. (2017). ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Applied Soft Computing, 60, 775-785. Van CalsterT.BaesensB.LemahieuW. (2017). ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Applied Soft Computing, 60, 775-785. Search in Google Scholar

[24] Mao Shuai & Huang Rong. (2021). Complexity Construction of Intelligent Marketing Strategy Based on Mobile Computing and Machine Learning Simulation Environment. COMPLEXITY. MaoShuaiHuangRong (2021). Complexity Construction of Intelligent Marketing Strategy Based on Mobile Computing and Machine Learning Simulation Environment. COMPLEXITY. Search in Google Scholar

[25] Gao Yu. (2020). Business intelligent marketing decision support system based on data warehouse. BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 28-29. GaoYu (2020). Business intelligent marketing decision support system based on data warehouse. BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 28-29. Search in Google Scholar

[26] Mbulelo Phesa, Nkanyiso Mbatha & Akinola Ikudayisi. (2024). MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa. Hydrology, 10, 176-176. PhesaMbuleloMbathaNkanyisoIkudayisiAkinola (2024). MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa. Hydrology, 10, 176-176. Search in Google Scholar