Co-Movement Clustering: A Novel Approach for Predicting Inflation in the Food and Beverage Industry
DOI:
https://doi.org/10.32890/jeth2023.3.1Keywords:
Inflation Rate Prediction, Clustering Technique, Time Series Analysis, Food and Beverage, Hospitality IndustryAbstract
In the realm of food and beverage businesses, inflation poses a significant hurdle as it affects pricing, profitability, and consumer’s purchasing power, setting it apart from other industries. This study proposes a novel approach; co-movement clustering, to predict which items will be inflated together according to historical time-series data. Experiments were conducted to evaluate the proposed approach based on real-world data obtained from the UK Office for National Statistics. The predicted results of the proposed approach were compared against four classical methods (correlation, Euclidean distance, Cosine Similarity, and DTW). According to our experimental results, the accuracy of the proposed approach outperforms the above-mentioned classical methods. Moreover, the accuracy of the proposed approach is higher when an additional filter is applied. Our approach aids hospitality operators in accurately predicting food and beverage inflation, enabling the development of effective strategies to navigate the current challenging business environment in hospitality management. The lack of previous work has explored how time series clustering can be applied to support inflation prediction. This study opens a new research paradigm to the related field and this study can serve as a useful reference for future research in this emerging area. In addition, this study work contributes to the data analytics research stream in hospitality management literature.
Downloads
References
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015). Time-series clustering – A decade review. Information Systems, 53, 16-38. https://doi.org/https://doi.org/10.1016/j.is.2015.04.
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications Proceedings of the 1998 ACM SIGMOD International Conference on Management of data, Seattle, Washington, USA. https://doi.org/10.1145/276304.276314
Ali, M. H., Iranmanesh, M., Tan, K. H., Zailani, S., & Omar, N. A. (2022). Impact of supply chain integration on halal food supply chain integrity and food quality performance. Journal of Islamic Marketing, 13(7), 1515-1534.
Aljawarneh, S., Radhakrishna, V., Kumar, P. V., & Janaki, V. (2016, 22-24 Sept. 2016). A similarity measure for temporal pattern discovery in time series data generated by IoT. 2016 International Conference on Engineering & MIS (ICEMIS).
Anh, D. T., & Thanh, L. H. (2015). An efficient implementation of k-means clustering for time series data with DTW distance. International Journal of Business Intelligence and Data Mining, 10(3), 213-232. https://doi.org/10.1504/IJBIDM.2015.071311
Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure Proceedings of the 1999 ACM SIGMOD international conference on Management of data, Philadelphia, Pennsylvania, USA. https://doi.org/10.1145/304182.304187
Atalan, A. (2023). Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms. Agribusiness, 39(1), 214-241.
Berthold, M. R., & Höppner, F. (2016). On clustering time series using euclidean distance and pearson correlation. arXiv:1601.02213. https://doi.org/10.48550/ARXIV.1601.02213
Cheeseman, P., & Stutz, J. (1996). Bayesian classification (AutoClass): theory and results," in advances in knowledge discovery and data mining. In U. M. Fayyad, G. Piatetsky-Shapiro, S. P., & R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 153-180). MIT Press.
Chen, J. R. (2005, 2005). Making subsequence time series clustering meaningful. Fifth IEEE International Conference on Data Mining (ICDM'05).
D’Acunto, F., Malmendier, U., Ospina, J., & Weber, M. (2021). Exposure to grocery prices and inflation expectations. Journal of Political Economy, 129(5), 1615-1639.
Deloitte. (2022). Summer 2022 Fortune/Deloitte CEO Survey. https://www2.deloitte.com/us/en/pages/chief-executive-officer/articles/ceo-survey.html
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22. https://doi.org/https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
Diazgranados, D. (2022). 3 Ways Inflation is Impacting the Food and Beverage Industry. Entrepreneur. https://www.entrepreneur.com/money-finance/3-ways-inflation-is-impacting-the-food-and-beverage-industry/435900
Egri, A., Horváth, I., Kovács, F., Molontay, R., & Varga, K. (2017, 20-23 Oct. 2017). Cross-correlation based clustering and dimension reduction of multivariate time series. 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES).
Ester, M., Kriegel, H., J., S., & Xu, X. (1996, 1996 Aug 2). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of KDD-96.
Focardi, S. M., & Fabozzi, F. J. (2001). Clustering economic and financial time series: Exploring the existence of stable correlation conditions. Discussion Paper. https://www.researchgate.net/profile/SergioFocardi/publication/2547390_Clustering_economi c_and_financial_time_series_Exploring_the_existence_of_stable_correlation_conditions/links /56b379c608ae5deb2657d5ba/Clustering-economic-and-financial-time-series-Exploring-the-existence-of-stable-correlation-conditions.pdf
Forgy, E. W. (1965). Cluster analysis of multivariate data: Efficiency versus interpretability of classifications. Biometrics, 21, 768-769.
Goldman Sachs. (2022). Survey: Small business challenges worsen amid record inflation and workforce shortages. Retrieved 30 Sept 2022 from https://www.goldmansachs.com/citizenship/10000-small-businesses/US/infographics/small-businesses-fear-looming-recession/index.html
González-Vidal, A., Barnaghi, P., & Skarmeta, A. F. (2018). BEATS: Blocks of Eigenvalues Algorithm for Time Series Segmentation. IEEE Transactions on Knowledge and Data Engineering, 30(11), 2051-2064. https://doi.org/10.1109/TKDE.2018.2817229
Gopikrishnan, P., Plerou, V., Liu, Y., Amaral, L. A. N., Gabaix, X., & Stanley, H. E. (2000). Scaling and correlation in financial time series. Physica A: Statistical Mechanics and its Applications, 287(3), 362-373. https://doi.org/https://doi.org/10.1016/S0378-4371(00)00375-
Graves, D., & Pedrycz, W. (2010,. 2010). Proximity fuzzy clustering and its application to time series clustering and prediction. 2010 10th International Conference on Intelligent Systems Design and Applications.
Guha, S., Rastogi, R., & Shim, K. (1998). CURE: An efficient clustering algorithm for large databases Proceedings of the 1998 ACM SIGMOD international conference on Management of data, Seattle, Washington, USA. https://doi.org/10.1145/276304.276312
Guo, Y., Tang, D., Tang, W., Yang, S., Tang, Q., Feng, Y., & Zhang, F. (2022). Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors. Sustainability, 14(17), 10483.
Guoqing, C., Qiang, W., & Hong, Z. (2001, 2001). Discovering similar time-series patterns with fuzzy clustering and DTW methods. Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).
He, W., Feng, G., Wu, Q., He, T., Wan, S., & Chou, J. (2012). A new method for abrupt dynamic change detection of correlated time series. International Journal of Climatology, 32(10), 1604-1614. https://doi.org/https://doi.org/10.1002/joc.2367
Hotstats.(2022). Hotels Are Filling Up. Guest Stomachs, Too. https://www.hotstats.com/blog/hotels-are-filling-up-guest-stomachs-too
Ito, F., Hiroyasu, T., Miki, M., & Yokouchi, H. (2009, 20-24 Aug. 2009). Detection of preference shift timing using time-series clustering. 2009 IEEE International Conference on Fuzzy Systems.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM Comput. Surv., 31(3), 264–323. https://doi.org/10.1145/331499.331504
JP Morgan. (2022). Midyear Survey: Business optimism falls to record lows. https://www.jpmorgan.com/commercial-banking/insights/business-leaders-outlook-pulse-survey
Kalpakis, K., Gada, D., & Puttagunta, V. (2001, 29 Nov.-2 Dec. 2001). Distance measures for effective clustering of ARIMA time-series. Proceedings 2001 IEEE International Conference on Data Mining.
Karypis, G., Eui-Hong, H., & Kumar, V. (1999). Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8), 68-75. https://doi.org/10.1109/2.781637
Kaufman, L., & Rousseeuw, P. J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons Inc.
Klein, J. L. (1997). Statistical visions in time: a history of time series analysis 1662-1938. Cambridge University Press.
Kubatko, O., Merritt, R., Duane, S., & Piven, V. (2023). The impact of the COVID-19 pandemic on global food system resilience. Mechanism of an Economic Regulation, 1(99), 144-148.
Kumar, R. P., & Nagabhushan, P. (2006). Time Series as a Point-A Novel Approach for Time Series Cluster Visualization. DMIN.
Lkhagva, B., Yu, S., & Kawagoe, K. (2006, 2006). New Time Series Data Representation ESAX for Financial Applications. 22nd International Conference on Data Engineering Workshops (ICDEW'06).
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability.
McAllister, J. (2022). Operators face 'significant challenge managing costs and attracting footfall' as menu sizes and prices rise. BigHospitality. from https://www.bighospitality.co.uk/Article/2022/05/20/Hospitality-operators-face-significant-challenge-managing-costs-and-attracting-footfall-as-menu-sizes-and-prices-rise
McLachlan, G. J., & Basford, K. E. (1988). Mixture models: Inference and applications to clustering (Vol. 38). New York: M. Dekker.
Murugesan, G., & Radha, B. (2023). An extrapolative model for price prediction of crops using hybrid ensemble learning techniques. International Journal of Advanced Technology and Engineering Exploration, 10(98), 1.
Ng, R. T., & Han, J. (2014, 1994). Efficient and effective clustering methods for spatial data mining. Proceedings of the 20th International Conference on very Large Data Bases.
Oliveira, A. L., & Antunes, C. M. (2001). Temporal data mining: An overview. KDD Workshop on Temporal Data Mining.
Pavlidis, N. G., Plagianakos, V. P., Tasoulis, D. K., & Vrahatis, M. N. (2006). Financial forecasting through unsupervised clustering and neural networks. Operational Research, 6(2), 103-127. https://doi.org/10.1007/BF02941227
Ramirez, L. (2022, 2022). Rising food prices taking a toll on Bay Area restaurant owners. CBS News Bay Area. https://www.cbsnews.com/sanfrancisco/news/rising-food-prices-taking-a-toll-on-restaurant-owners/
Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. https://doi.org/10.1109/TASSP.1978.1163055
Sarangi, P. K., Sinha, D., Sinha, S., & Mittal, N. (2021). Machine learning approach for the prediction of consumer food price index. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO). 1-6. IEEE.
Sfetsos, A., & Siriopoulos, C. (2004). Time series forecasting with a hybrid clustering scheme and pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 34(3), 399-405. https://doi.org/10.1109/TSMCA.2003.822270
Sheikholeslami, G., Chatterjee, S., & Zhang, A. (1998). Wavecluster: A multi-resolution clustering approach for very large spatial databases. Proceedings of the 24th Conference on very Large Databases.
Siyou Fotso, V. S., Mephu Nguifo, E., & Vaslin, P. (2020). Frobenius correlation based u-shapelets discovery for time series clustering. Pattern Recognition, 103, 107301. https://doi.org/https://doi.org/10.1016/j.patcog.2020.107301
Venkateswara Rao, K., Srilatha, D., Jagan Mohan Reddy, D., Desanamukula, V. S., & Kejela, M. L. (2022). Regression based price prediction of staple food materials using multivariate models. Scientific Programming, 2022. https://doi.org/10.1155/2022/4572064
Wang, W., Yang, J., & Muntz, R. (1997). STING: A statistical information grid approach to spatial data mining. Proceedings of 23rd Conference on Very Large Databases.
Wang, X., Smith, K. A., Hyndman, R., & Alahakoon, D. (2004). A Scalable Method for Time Series Clustering (Technical Report Monash University).
Watson, N. J., Bowler, A. L., Rady, A., Fisher, O. J., Simeone, A., Escrig, J., & Adedeji, A. A. (2021). Intelligent sensors for sustainable food and drink manufacturing. Frontiers in Sustainable Food Systems, 5, 642786.
Wu, Y., Liu, S., Smith, K., & Wang, X. (2018). Using correlation between data from multiple monitoring sensors to detect bursts in water distribution systems. Journal of Water Resources Planning and Management, 144(2), 04017084. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000870
Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases Proceedings of the 1996 ACM SIGMOD International Conference on Management of data, Montreal, Quebec, Canada. https://doi.org/10.1145/233269.233324
Published
Issue
Section
License
Copyright (c) 2023 Journal of Event, Tourism and Hospitality Studies
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Research impact
Harvested 2026-09-14Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
