Forecasting Turkey's Hazelnut Export Quantities with Facebook's Prophet Algorithm and Box-Cox Transformation
Abstract Time series forecasting methods are used by an evolving field of data analytics for the prediction of market trends, sales, and demands. Turkey is the major producer of hazelnut in the world. If Turkey wants to continue its domination of hazelnut and protect the price-setting role, time series forecasting methods could be key factors accordingly. There are a few studies that focused on time series forecasting of hazelnut export quantities of Turkey, and this study uses a recently developed algorithm and implements a power transformation to increase the forecast accuracy. The presented research aims to forecast Turkey’s hazelnut export quantities for the coming 36-months starting from June 2020. The forecasting process was conducted with the help of Facebook’s Prophet algorithm. To improve the forecast accuracy, a Box-Cox power transformation was also implemented to process. To find out the stationarity and periodicity of the data set, the Augmented Dickey-Fuller test and autocorrelation function was applied to the time-series data. The Prophet algorithm, with Box-Cox transformation, projected the hazelnut export quantity could be over five hundred thousand tons from 07/2020 to 06/2023. The export quantities were in an increment trend, and the slope of the trend has increased since June 2008 by 0.66 % per month. The Prophet algorithm also revealed the seasonality of the data set, and the export amounts indicate monthly oscillations. The monthly export volumes start to increase and reach their peak value in October because August is the time for the harvest of hazelnuts in Turkey.
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Acaröz Candan, S., Sahin, U. K., and Ako?lu, S., 2019. The investigation of work-related musculoskeletal disorders among female workers in a hazelnut factory: Prevalence, working posture, work-related and psychosocial factors. International Journal of Industrial Ergonomics ,74: 102838.
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Ascari, L., Siniscalco, C., Palestini, G., Lisperguer, M. J., Suarez Huerta, E., De Gregorio, T., and Bregaglio, S., 2020. Relationships between yield and pollen concentrations in Chilean hazelnut orchards. European Journal of Agronomy, 115: 126036.
Bars, T., Uçum, I., and Akbay, C., 2018. ARIMA Modeli ile Türkiye F?nd?k Üretim Projeksiyonu (In Turkish). Kahramanmara? Sütçü ?mam Üniversitesi Tar?m ve Do?a Dergisi, 21(Special Issue): 154-160.
Bhardwaj, S., Chandrasekhar, E., Padiyar, P., and Gadre, V. M., 2020. A comparative study of wavelet-based ANN and classical techniques for geophysical time-series forecasting. Computers & Geosciences, 138: 104461.
Bicego, M., and Baldo, S., 2016. Properties of the Box–Cox transformation for pattern classification. Neurocomputing, 218: 390-400.
Brockwell, P. J., and Davis, R. A., 2016. Introduction to Time Series and Forecasting. Switzerland, Springer International Publishing.
Carroll, R. J., and Ruppert, D., 1981. On prediction and the power transformation family. Biometrika, 68(3): 609-615.
Celenk, V. U., Argon, Z. U., and Gumus, Z. P., 2020. Chapter 20 - Cold pressed hazelnut (Corylus avellana) oil. Cold Pressed Oils. M. F. Ramadan, Academic Press: 241-254.
Çetinba?-Genç, A., Cai, G., Vardar, F., and Ünal, M., 2019. Differential effects of low and high temperature stress on pollen germination and tube length of hazelnut (Corylus avellana L.) genotypes. Scientia Horticulturae, 255: 61-69.
Cryer, J. D., and Chan, K.-S., 2008. Time Series Analysis - With Applications in R. New York, Springer-Verlag.
de Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F., 2016. Mean Absolute Percentage Error for regression models. Neurocomputing, 192: 38-48.
Erinjeri, J., Kastango, N., Flood, L., Gazit, L., Brody, L., Mohabir, H., and Solomon, S., 2020. Reduction of unplanned late hours in inpatient procedure scheduling by forecasting with the Facebook Prophet algorithm. Journal of Vascular and Interventional Radiology, 31(3): 151-152.
Facebook, Prophet. Retrieved on 20th May, 2020, from https://facebook.github.io/prophet/.
Fructuoso da Costa, A., and Fernando Crepaldi, A., 2014. The bias in reversing the Box–Cox transformation in time series forecasting: An empirical study based on neural networks. Neurocomputing, 136: 281-288.
Gonçalves, S., and Meddahi, N., 2011. Box–Cox transforms for realized volatility. Journal of Econometrics, 160(1): 129-144.
He, Y., Zheng, Y., and Xu, Q., 2019. Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods. Measurement, 136: 579-593.
Heiberger, R. M., and Holland, B., 2015. Statistical Analysis and Data Display - An Intermediate Course with Examples in R. New York, Springer-Verlag.
Ho?gün, E. Z., Berikten, D., K?vanç, M., and Bozan, B., 2017. Ethanol production from hazelnut shells through enzymatic saccharification and fermentation by low-temperature alkali pretreatment. Fuel, 196: 280-287.
Howarth, R. J., and Earle, S. A. M., 1979. Application of a generalized power transformation to geochemical data. Journal of the International Association for Mathematical Geology, 11(1): 45-62.
Meloun, M., Sá?ka, M., N?mec, P., K?ítková, S., and Kupka, K., 2005. The analysis of soil cores polluted with certain metals using the Box–Cox transformation. Environmental Pollution, 137(2): 273-280.
Neusser, K., 2016. Time Series Econometrics. Switzerland, Springer International Publishing.
Onal-Ulusoy, B., Sen, Y., and Mutlu, M., 2019. Quality changes of hazelnut kernels subjected to different cold plasmas and gamma irradiation treatments. LWT, 116: 108549.
Osborne, J. W., 2010. Improving your data transformations: Applying the Box-Cox transformation Practical Assessment, Research & Evaluation, 15(12): 1-9.
Papacharalampous, G., and Tyralis, H., 2020. Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability. Journal of Hydrology, 590: 125205.
Park, J. C., Chang, B. P., and Mok, N., 2019. 144 Time Series Analysis and Forecasting Daily Emergency Department Visits Utilizing Facebook’s Prophet Method. Annals of Emergency Medicine, 74(4): 57.
Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A., 2020. Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data. Pattern Recognition Letters, 136: 183-189.
Peng, Y., Feng, T., and Timmermans, H. J. P., 2019. Expanded comfort assessment in outdoor urban public spaces using Box-Cox transformation. Landscape and Urban Planning, 190: 103594.
Ramos Castro, N., and Swart, J., 2017. Building a roundtable for a sustainable hazelnut supply chain. Journal of Clean Production, 168: 1398-1412.
Ruppert, D., and Matteson, D. S., 2015. Statistics and Data Analysis for Financial Engineering - with R examples. New York, Springer-Verlag.
Scipy, Box-Cox Transformation. Retrieved on July 18th, 2020, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html#scipy.stats.boxcox.
Sen, Y., Onal-Ulusoy, B., and Mutlu, M., 2019. Aspergillus decontamination in hazelnuts: Evaluation of atmospheric and low-pressure plasma technology. Innovative Food Science & Emerging Technologies, 54: 235-242.
?enol, H., and Zenk, H., 2020. Determination of the biogas potential in cities with hazelnut production and examination of potential energy savings in Turkey. Fuel, 270: 117577.
Sharma, N., Singh, S. K., Mahato, A. K., Ravishankar, H., Dubey, A. K., and Singh, N. K., 2019. Physiological and molecular basis of alternate bearing in perennial fruit crops. Sci Hortic-Amsterdam, 243: 214-225.
Ta?, N. G., Y?lmaz, C., and Gökmen, V., 2019. Investigation of serotonin, free and protein-bound tryptophan in Turkish hazelnut varieties and effect of roasting on serotonin content. Food Res Int, 120: 865-871.
Taylor, N., 2017. Realised variance forecasting under Box-Cox transformations. International Journal of Forecasting, 33(4): 770-785.
Taylor, S. J., and Letham, B., 2017. Forecasting at Scale. PeerJ Preprints.
Tefek, M. F., U?uz, H., and Güçyetmez, M., 2019. A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey. Neural Computing and Applications, 31(7): 2939-2954.
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Tunçil, Y. E., 2020. Dietary fibre profiles of Turkish Tombul hazelnut (Corylus avellana L.) and hazelnut skin. Food Chemistry, 316: 126338.
Uzundumlu, A. S., Bilgiç, A. and Ertek, N., (2019). Türkiye’nin f?nd?k üretiminde önde gelen illerin 2019-2025 y?llar? aras?ndaki f?nd?k üretimlerinin ARIMA modeliyle tahmin edilmesi (In Turkish). Akademik Ziraat Dergisi, 8(Special Issue): 115-126.
Voyant, C., Notton, G., Duchaud, J.-L., Almorox, J., and Yaseen, Z. M., 2020. Solar irradiation prediction intervals based on Box–Cox transformation and univariate representation of periodic autoregressive model. Renewable Energy Focus, 33: 43-53.
Zhao, N., Liu, Y., Vanos, J. K., and Cao, G., 2018. Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure. Atmospheric Environment, 192: 116-127.
Adhikari, R., and Agrawal, R. K., 2013. An Introductory Study on Time Series Modeling and Forecasting, Lap Lambert Academic Publishing GmbH KG.
Ascari, L., Siniscalco, C., Palestini, G., Lisperguer, M. J., Suarez Huerta, E., De Gregorio, T., and Bregaglio, S., 2020. Relationships between yield and pollen concentrations in Chilean hazelnut orchards. European Journal of Agronomy, 115: 126036.
Bars, T., Uçum, I., and Akbay, C., 2018. ARIMA Modeli ile Türkiye F?nd?k Üretim Projeksiyonu (In Turkish). Kahramanmara? Sütçü ?mam Üniversitesi Tar?m ve Do?a Dergisi, 21(Special Issue): 154-160.
Bhardwaj, S., Chandrasekhar, E., Padiyar, P., and Gadre, V. M., 2020. A comparative study of wavelet-based ANN and classical techniques for geophysical time-series forecasting. Computers & Geosciences, 138: 104461.
Bicego, M., and Baldo, S., 2016. Properties of the Box–Cox transformation for pattern classification. Neurocomputing, 218: 390-400.
Brockwell, P. J., and Davis, R. A., 2016. Introduction to Time Series and Forecasting. Switzerland, Springer International Publishing.
Carroll, R. J., and Ruppert, D., 1981. On prediction and the power transformation family. Biometrika, 68(3): 609-615.
Celenk, V. U., Argon, Z. U., and Gumus, Z. P., 2020. Chapter 20 - Cold pressed hazelnut (Corylus avellana) oil. Cold Pressed Oils. M. F. Ramadan, Academic Press: 241-254.
Çetinba?-Genç, A., Cai, G., Vardar, F., and Ünal, M., 2019. Differential effects of low and high temperature stress on pollen germination and tube length of hazelnut (Corylus avellana L.) genotypes. Scientia Horticulturae, 255: 61-69.
Cryer, J. D., and Chan, K.-S., 2008. Time Series Analysis - With Applications in R. New York, Springer-Verlag.
de Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F., 2016. Mean Absolute Percentage Error for regression models. Neurocomputing, 192: 38-48.
Erinjeri, J., Kastango, N., Flood, L., Gazit, L., Brody, L., Mohabir, H., and Solomon, S., 2020. Reduction of unplanned late hours in inpatient procedure scheduling by forecasting with the Facebook Prophet algorithm. Journal of Vascular and Interventional Radiology, 31(3): 151-152.
Facebook, Prophet. Retrieved on 20th May, 2020, from https://facebook.github.io/prophet/.
Fructuoso da Costa, A., and Fernando Crepaldi, A., 2014. The bias in reversing the Box–Cox transformation in time series forecasting: An empirical study based on neural networks. Neurocomputing, 136: 281-288.
Gonçalves, S., and Meddahi, N., 2011. Box–Cox transforms for realized volatility. Journal of Econometrics, 160(1): 129-144.
He, Y., Zheng, Y., and Xu, Q., 2019. Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods. Measurement, 136: 579-593.
Heiberger, R. M., and Holland, B., 2015. Statistical Analysis and Data Display - An Intermediate Course with Examples in R. New York, Springer-Verlag.
Ho?gün, E. Z., Berikten, D., K?vanç, M., and Bozan, B., 2017. Ethanol production from hazelnut shells through enzymatic saccharification and fermentation by low-temperature alkali pretreatment. Fuel, 196: 280-287.
Howarth, R. J., and Earle, S. A. M., 1979. Application of a generalized power transformation to geochemical data. Journal of the International Association for Mathematical Geology, 11(1): 45-62.
Meloun, M., Sá?ka, M., N?mec, P., K?ítková, S., and Kupka, K., 2005. The analysis of soil cores polluted with certain metals using the Box–Cox transformation. Environmental Pollution, 137(2): 273-280.
Neusser, K., 2016. Time Series Econometrics. Switzerland, Springer International Publishing.
Onal-Ulusoy, B., Sen, Y., and Mutlu, M., 2019. Quality changes of hazelnut kernels subjected to different cold plasmas and gamma irradiation treatments. LWT, 116: 108549.
Osborne, J. W., 2010. Improving your data transformations: Applying the Box-Cox transformation Practical Assessment, Research & Evaluation, 15(12): 1-9.
Papacharalampous, G., and Tyralis, H., 2020. Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability. Journal of Hydrology, 590: 125205.
Park, J. C., Chang, B. P., and Mok, N., 2019. 144 Time Series Analysis and Forecasting Daily Emergency Department Visits Utilizing Facebook’s Prophet Method. Annals of Emergency Medicine, 74(4): 57.
Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A., 2020. Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data. Pattern Recognition Letters, 136: 183-189.
Peng, Y., Feng, T., and Timmermans, H. J. P., 2019. Expanded comfort assessment in outdoor urban public spaces using Box-Cox transformation. Landscape and Urban Planning, 190: 103594.
Ramos Castro, N., and Swart, J., 2017. Building a roundtable for a sustainable hazelnut supply chain. Journal of Clean Production, 168: 1398-1412.
Ruppert, D., and Matteson, D. S., 2015. Statistics and Data Analysis for Financial Engineering - with R examples. New York, Springer-Verlag.
Scipy, Box-Cox Transformation. Retrieved on July 18th, 2020, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html#scipy.stats.boxcox.
Sen, Y., Onal-Ulusoy, B., and Mutlu, M., 2019. Aspergillus decontamination in hazelnuts: Evaluation of atmospheric and low-pressure plasma technology. Innovative Food Science & Emerging Technologies, 54: 235-242.
?enol, H., and Zenk, H., 2020. Determination of the biogas potential in cities with hazelnut production and examination of potential energy savings in Turkey. Fuel, 270: 117577.
Sharma, N., Singh, S. K., Mahato, A. K., Ravishankar, H., Dubey, A. K., and Singh, N. K., 2019. Physiological and molecular basis of alternate bearing in perennial fruit crops. Sci Hortic-Amsterdam, 243: 214-225.
Ta?, N. G., Y?lmaz, C., and Gökmen, V., 2019. Investigation of serotonin, free and protein-bound tryptophan in Turkish hazelnut varieties and effect of roasting on serotonin content. Food Res Int, 120: 865-871.
Taylor, N., 2017. Realised variance forecasting under Box-Cox transformations. International Journal of Forecasting, 33(4): 770-785.
Taylor, S. J., and Letham, B., 2017. Forecasting at Scale. PeerJ Preprints.
Tefek, M. F., U?uz, H., and Güçyetmez, M., 2019. A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey. Neural Computing and Applications, 31(7): 2939-2954.
Trade-Map. Montly Hazelnut Export Values. Retrieved on July 6th, 2020, from https://www.trademap.org/.
TUIK. Data Portal of Turkish Republic. Retrieved on 2 February, 2020, from https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr.
Tunçil, Y. E., 2020. Dietary fibre profiles of Turkish Tombul hazelnut (Corylus avellana L.) and hazelnut skin. Food Chemistry, 316: 126338.
Uzundumlu, A. S., Bilgiç, A. and Ertek, N., (2019). Türkiye’nin f?nd?k üretiminde önde gelen illerin 2019-2025 y?llar? aras?ndaki f?nd?k üretimlerinin ARIMA modeliyle tahmin edilmesi (In Turkish). Akademik Ziraat Dergisi, 8(Special Issue): 115-126.
Voyant, C., Notton, G., Duchaud, J.-L., Almorox, J., and Yaseen, Z. M., 2020. Solar irradiation prediction intervals based on Box–Cox transformation and univariate representation of periodic autoregressive model. Renewable Energy Focus, 33: 43-53.
Zhao, N., Liu, Y., Vanos, J. K., and Cao, G., 2018. Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure. Atmospheric Environment, 192: 116-127.
Aytaç, E. (2021). Forecasting Turkey’s Hazelnut Export Quantities with Facebook’s Prophet Algorithm and Box-Cox Transformation. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(1), 33–47. https://doi.org/10.14201/ADCAIJ20211013347
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