Demand forecasting: an alternative approach based on technical indicator Pbands
DOI:
https://doi.org/10.24136/oc.2021.035Keywords:
demand forecasting, neural network, BATS, hybrid model, PbandsAbstract
Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure.
Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries.
Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator. The study uses MAPE and RMSE approaches to measure the accuracy.
Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.
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Altin, F. G., & Celik, E. (2020). Monthly container demand forecast for port of antalya using gray prediction and Box-Jenkins methods. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 7(3), 540? 562. doi: 10.30798/makuiibf.689532. DOI: https://doi.org/10.30798/makuiibf.689532
View in Google Scholar
Assimakopoulos, V. N. (2000). The Theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16(4), 520?530. doi: 10.1016 /S0169-2070(00)00066-2. DOI: https://doi.org/10.1016/S0169-2070(00)00066-2
View in Google Scholar
Babai, M. Z., Tsadiras, A., & Papadopoulos, C. (2020). On the empirical performance of some new neural network methods for forecasting intermittent demand. IMA Journal of Management Mathematics, 31(3), 281?305. doi: 10.10 93/imaman/dpaa003. DOI: https://doi.org/10.1093/imaman/dpaa003
View in Google Scholar
Bokelmann, B., & Lessmann, S. (2019). Spurious patterns in Google Trends data - an analysis of the effects on. Tourism Management, 75, 1?12. doi: 10.1016/j.to urman.2019.04.015. DOI: https://doi.org/10.1016/j.tourman.2019.04.015
View in Google Scholar
Brown, R. G. (1959). Statistical forecasting for inventory control. New York: McGraw-Hill.
View in Google Scholar
Bruzda, J. (2020). Demand forecasting under fill rate constraints?the case of re-order points. International Journal of Forecasting, 36, 1342?1361. doi: 10.101 6/j.ijforecast.2020.01.007. DOI: https://doi.org/10.1016/j.ijforecast.2020.01.007
View in Google Scholar
Cerqueira, V., Torgo, L., & Soares, C. (2019). Machine learning vs statistical methods for time series forecasting: size matters. ArXiv, abs/1909.13316. Machine Learning. Retrieved from arXiv:1909.13316.
View in Google Scholar
Civelek, M., Ključnikov, A., Fialova, V., Folvarčná, A., & Stoch, M. (2021). How innovativeness of family-owned SMEs differ depending on their characteris-tics? Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(2), 413?428. doi: 10.24136/eq.2021.015. DOI: https://doi.org/10.24136/eq.2021.015
View in Google Scholar
De Livera,A., Hyndman, R. J., & Snydera, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513?1527. doi: 10.1198/jasa.201 1.tm09771. DOI: https://doi.org/10.1198/jasa.2011.tm09771
View in Google Scholar
Gabor, M., & Dorgo, L. (2017). Neural networks versus box-jenkins method for turnover forecasting: a case study on the romanian organisation. Transformations in Business & Economics, 16(1), 187?210.
View in Google Scholar
Haykin, S. (1994). Neural networks: a comprehensive foundation. New York: Macmillan College Publishing Company.
View in Google Scholar
Holt, C. C. (1957). Forecasting seasonals and trends byexponentially weighted moving averages. In ONR memorandum, 52. Pittsburgh: Carnegie Institute of Technology.
View in Google Scholar
Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., Yasmeen, F., R Core Team, Ihaka, R., Reid, D., Shaub, D., Tang, Y., Zhou, Z. (2021). Forecast: forecasting functions for time series and linear models. Retrieved from https://CRAN.R-project.org/package=forecast.
View in Google Scholar
Hyndman, R., & Fan, S. (2010). Density forecasting for long-term peak electricity demand. IEEE Transactions on Power Systems, 25(2), 1142?1153. doi: 10.110 9/TPWRS.2009.2036017. DOI: https://doi.org/10.1109/TPWRS.2009.2036017
View in Google Scholar
Hyndman, R., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 27(3), 1?22. doi: 10.186 37/jss.v027.i03. DOI: https://doi.org/10.18637/jss.v027.i03
View in Google Scholar
Hyndman, R., & Koehler, A. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679?688. doi: 10.1016/j. ijforecast.2006.03.001. DOI: https://doi.org/10.1016/j.ijforecast.2006.03.001
View in Google Scholar
Choi S. B., & Ahn I. (2020). Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina. PLoS ONE, 15(7), e0233855. doi: 10.1371/journal.pone.0233855. DOI: https://doi.org/10.1371/journal.pone.0233855
View in Google Scholar
Janurová, K., Litschmannova, M., Skopal, R., Kuranová, P., & Beloch, M. (2016). Supporting freeware for statistical lectures - RKward. In 10th international days of statistics and economics. Prague: Melandrium, 711?722.
View in Google Scholar
Karadzic, V. P., & Pejovic, B. (2020). Tourism demand forecasting using ARIMA model. Transformations in Business & Economics, 19(2), 731?745.
View in Google Scholar
Khan, M. A., Yasir, M., & Khan, M. A. (2021). Factors affecting customer loyalty in the services sector. Journal of Tourism and Services, 22(12), 184?197. doi: 10.29036/jots.v12i22.257. DOI: https://doi.org/10.29036/jots.v12i22.257
View in Google Scholar
Ključnikov, A., Civelek, M., Fialova, V., & Folvarčná, A. (2021). Organizational, local, and global innovativeness of family-owned SMEs depending on firm-individual level characteristics: evidence from the Czech Republic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(1), 169?184. doi: 10.24136/eq.2021.006. DOI: https://doi.org/10.24136/eq.2021.006
View in Google Scholar
Kolková, A. (2016). Back - test of efficiency by combining technical indicators on the EUR/JPY. In Financial management of firms and financial institutions. 11th international scientific conference. Ostrava: VŠB - TU Ostrava, 391?399.
View in Google Scholar
Kolková, A. (2018). Measuring the accuracy of quantitative prognostic methods and methods based on technical indicators in the field of tourism. Journal Acta Oeconomica Universitatis Selye, 7(1), 58?70.
View in Google Scholar
Kolková, A. (2019). Aplication of artificial neural networks for forecasting in business. In 7th international conference on innovation management, entrepreneurship and sustainability (IMES). Praha: VŠE Praha, 359?368.
View in Google Scholar
Kolková, A. (2020). The application of forecasting sales of services to increase business competitiveness. Journal of Competitiveness, 12(2), 90?105. doi: 10.7 441/joc.2020.02.06. DOI: https://doi.org/10.7441/joc.2020.02.06
View in Google Scholar
Kremer, M. S. (2016). The sum and its parts: judgmental hierarchical forecasting. Management Science, 62(9), 2457?2764. doi: 10.1287/mnsc.2015.2259. DOI: https://doi.org/10.1287/mnsc.2015.2259
View in Google Scholar
Lin, H., & Lin, C. (2021). Establishing a combined forecasting model: a case study on the logistic demand of nanjing?s green tea industry in china. Technological and Economic Development of Economy, 27(1), 71?95. doi: 10.3846/tede.2020 .14008. DOI: https://doi.org/10.3846/tede.2020.14008
View in Google Scholar
Machová, R., Korcsmáros, E., Esseová, M., & Marča R. (2021). Changing trends of shopping habits and tourism during the second wave of COVID-19 ? international comparison. Journal of Tourism and Services, 22(12), 131?149. doi: 10.29036/jots.v12i22.256. DOI: https://doi.org/10.29036/jots.v12i22.256
View in Google Scholar
Makridakis, S., Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K., & Simmons, L. (1993). The M2-competition: a real-time judgmentally based forecasting study. International Journal of Forecasting, 9(1), 5?22. doi: 10.101 6/0169-2070(93)90044-N. DOI: https://doi.org/10.1016/0169-2070(93)90044-N
View in Google Scholar
Makridakis, S., & Hibon, M. (1979). Accuracy of forecasting: an empirical investigation (with discussion). Journal of the Royala Statistical Society, 142, 97?145. DOI: https://doi.org/10.2307/2345077
View in Google Scholar
Makridakis, S., & Hibon, G. (2000). The M3-competition: results, conclusions and implications. International Journal of Forecasting, 16(4), 451?476. doi: 10.10 16/S0169-2070(00)00057-1. DOI: https://doi.org/10.1016/S0169-2070(00)00057-1
View in Google Scholar
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 competition: results, findings, conclusion and way forward. International Journal of Forecasting, 34, 802?808. doi: 10.1016/j.ijforecast.2018.06.001. DOI: https://doi.org/10.1016/j.ijforecast.2018.06.001
View in Google Scholar
Montero-Manso, P., Athanasopoulos, G., Hyndman, R. J., & Talagala, T. S. (2020). FFORMA: feature-based forecast model averaging. International Journal of Forecasting, 36(1), 86?92. doi: 10.1016/j.ijforecast.2019.02.011. DOI: https://doi.org/10.1016/j.ijforecast.2019.02.011
View in Google Scholar
Navrátil, M., & Kolková, A. (2019). Decomposition and forecasting time series in business economy using prophet forecasting model. Central European Business Review, 8(4), 26?39. doi: 10.18267/j.cebr.221. DOI: https://doi.org/10.18267/j.cebr.221
View in Google Scholar
Nikolopoulos, K. (2003). Simplicity, inference and modelling: keeping it sophisti-catedly simple. International Journal of Forecasting, 19(2), 333?335. doi: 10.1016/S0169-2070(03)00018-9. DOI: https://doi.org/10.1016/S0169-2070(03)00018-9
View in Google Scholar
Nikolopoulos, K. (2021). We need to talk about intermittent demand forecasting. European Journal of Operational Research, 291 (2), 549?559. doi: 10.1016/j.ej or.2019.12.046. DOI: https://doi.org/10.1016/j.ejor.2019.12.046
View in Google Scholar
Pai, P., Hong, L., & Lin, K. (2018). Using Internet search trends and historical trad-ing data for predicting stock markets by the least squares support vector regres-sion model. Computational Intelligence and Neuroscience, 1(15). doi: 10.1 155/2018/6305246. DOI: https://doi.org/10.1155/2018/6305246
View in Google Scholar
Pedersen, T. L. (2020). Package 'ggplot2' (version 3.3.2). Retrieved from cloud.r-project.org: ggplot2.tidyverse.org,https://github.com/tidyverse/ggplot2.
View in Google Scholar
Rajput, V. P. (2020). A novel protection scheme for solar photovoltaic generator connected networks using hybrid harmony search algorithm-bollinger bands approach. Energies, 13(10). doi: 10.3390/en13102439. DOI: https://doi.org/10.3390/en13102439
View in Google Scholar
Roach, C., Hyndman, R., & Ben, T. S. (2021). Non-linear mixed-effects models for time series forecasting of smart meter demand. Journal of Forecasting. Advance online publicaton. doi: 10.1002/for.2750. DOI: https://doi.org/10.1002/for.2750
View in Google Scholar
Rostami-Tabar, B., Babai, M. Z., Ali, M., & Boylan, J. E. (2019). The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes. European Journal of Operational Research, 273(3), 920?932. doi: 10.1016/j.ej or.2018.09.010. DOI: https://doi.org/10.1016/j.ejor.2018.09.010
View in Google Scholar
Shao, J., Liang, C., Liu, Y., Xu, J., & Zhao, S. (2021). Relief demand forecasting based on intuitionistic fuzzy case-based reasoning. Socio-Economic Planning Sciences, 74, 100932. doi:10.1016/j.seps.2020.100932. DOI: https://doi.org/10.1016/j.seps.2020.100932
View in Google Scholar
Shaub, D. (2020). Fast and accurate yearly time series forecasting with forecast combinations. International Journal of Forecasting, 36(1), 116?120. doi: 10.10 16/j.ijforecast.2019.03.032. DOI: https://doi.org/10.1016/j.ijforecast.2019.03.032
View in Google Scholar
Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75?85. doi: 10.1016/j.ijforecast.2019.03.017. DOI: https://doi.org/10.1016/j.ijforecast.2019.03.017
View in Google Scholar
Souza, R. F., Wanke, P., & Correa, H. (2021). Demand forecasting in the beauty industry using fuzzy inference systems. Journal of Modelling in Management, 15(4), 1389?1417. doi: 10.1108/JM2-03-2019-0050. DOI: https://doi.org/10.1108/JM2-03-2019-0050
View in Google Scholar
Syntetos, A., Babai, Z., Boylan, J., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1?26. doi: 10.1016/j.ejor.2015.11.010. DOI: https://doi.org/10.1016/j.ejor.2015.11.010
View in Google Scholar
Šimeček, P. (2019). Statistical vs. deep learning methods for time series forecasting. Retrieved from http://www.mlmu.cz/archiv/
View in Google Scholar
Ulrich, J. (2020). Package TTR (version 0.24.2). Retrieved from https://CRAN.R-project.org/package=TTS.
View in Google Scholar
Vosen, S., & Schmidt, T.(2011). Forecasting private consumption: survey-based indicators vs. Google trends. Journal of Forecasting, 30(6), 565?578. doi: 10.1002/for.1213. DOI: https://doi.org/10.1002/for.1213
View in Google Scholar
Vergura, S. (2020). Bollinger bands based on exponential moving average for statistical monitoring of multi-array photovoltaic systems. Energies, 13(15). doi: 10.3390/en13153992. DOI: https://doi.org/10.3390/en13153992
View in Google Scholar
Winters, P. R. (1960). Forecasting sales by exponentially weightedmoving averages. Management Science, 6(3), 324?342. DOI: https://doi.org/10.1287/mnsc.6.3.324
View in Google Scholar
Zellner, A. (2001). Keep it sophisticatedly simple. In V. A. K. Zellner (Ed.) Simplicity, inference and modelling: keep it sophisticatedly simple. Cambridge: Cambridge University Press, 242?262. DOI: https://doi.org/10.1017/CBO9780511493164.014
View in Google Scholar
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