Abstract
In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, Y.M., Yu, H., Sun, Z.: Aircraft failure rate forecasting method based on Holt-Winters seasonal model. In: IEEE International Conference on Cloud Computing & Big Data Analysis (2017)
Assis, M.V.O.D., Hamamoto, A.H., Abrão, T., et al.: A game theoretical based system using Holt-Winters and genetic algorithm with fuzzy logic for DoS/DDoS mitigation on SDN Networks. IEEE Access 5(99), 9485–9496 (2017)
Li, G., Shuang, Y.: Study on construction of agricultural product supply chain and benefit distribution based on agricultural industrialization poverty relief project: taking Rosa sterilis processing industry as the example. In: International Conference on Service Systems & Service Management (2017)
Mahdian, E., Karazhian, R.: Effects of fat replacers and stabilizers on rheological, physicochemical and sensory properties of reduced-fat ice cream. J. Agric. Sci. Technol. 15(6), 1163–1174 (2018)
Yang, Z., Hu, C., Zhu, X.: The data analysis and the database construction for Pleurotus Eryngii product traceability system. In: International Conference on Information Science & Control Engineering (2017)
Yan, J.P., Huang, Y.F., Chua, K.C., et al.: River catchment rainfall series analysis using additive Holt-Winters method. J. Earth Syst. Sci. 125(2), 269–283 (2016)
Yan, Z., Fei, W., Jian, X.: Research on energy saving method of IDC CRAC system based on prediction of working load. In: International Conference on Smart & Sustainable City & Big Data (2016)
Rodriguez, H., Puig, V., Flores, J.J., et al.: Combined holt-winters and GA trained ANN approach for sensor validation and reconstruction: application to water demand flowmeters. In: Control & Fault-tolerant Systems (2016)
Razali, S.N.A.M., Rusiman, M.S., Zawawi, N.I., et al.: Forecasting of water consumptions expenditure using Holt-Winter’s and ARIMA. J. Phys. Conf. Ser. (2018)
Wu, L., Gao, X., Xiao, Y., et al.: Using grey Holt-Winters model to predict the air quality index for cities in China. Nat. Hazards 88(2), 1–10 (2017)
Dasgupta, S.S., Mahanta, P., Roy, R., et al.: Forecasting industry big data with Holt Winters method from a perspective of in-memory paradigm. In: Meersman, R., et al. (eds.) On the Move to Meaningful Internet Systems: OTM 2014 Workshops 2014. LNCS, vol. 8842, pp. 80–85. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45550-0_11
Fang, D., Yin, Z., Spicher, K.: Forecasting accuracy analysis based on two new heuristic methods and Holt-Winters-Method. In: IEEE International Conference on Big Data Analysis (2016)
Salamah, M., Kuswanto, H., Rachmi, A., et al.: Simulation study to construct the prediction interval for double seasonal Holt-Winters. Int. J. Appl. Math. Stat.™ 52(7), 43–50 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Deng, Y., Zhu, Y., Zhang, Q., Cheng, X. (2019). Hadoop + Spark Platform Based on Big Data System Design of Agricultural Product Price Analysis and Prediction by HoltWinters. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-0118-0_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0117-3
Online ISBN: 978-981-15-0118-0
eBook Packages: Computer ScienceComputer Science (R0)