Abstract
A proposed method, based on TL-LSTM, is presented for forecasting consumption data in college canteens. This method is compared to the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) method. While there has been some research on using the transfer learning (TL) method combined with the long short-term memory (LSTM) method in deep learning for time series forecasting, it is not extensive. The research focuses on the monthly student canteen consumption time series of two colleges in Zhaoqing City from 2014 to 2020, with a training set and validation set ratio of 8:2. The test set consists of data from September 2020 to March 2021. The college with a shorter time series is referred to as the target school, while the college with a longer time series is referred to as the source school. This paper utilizes the instance-based TL method to address the challenges of limited data and a lack of labeled data in the target school. The canteen transactions are then categorized according to certain standards. The consumption data of the target school is analysed and forecasted using a multivariate LSTM model in deep learning. The experimental results are as follows: (1) The training sample is sufficient, and the forecast accuracy is significantly improved with the application of instance-based TL. (2) Forecasting the actual value of canteen consumption data is challenging, and it is necessary to consider the total number of students. (3) The use of the TL method for forecasting time series of Campus One-card data is a novel idea with potential for further research.
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Data availability
The datasets generated during and/or analysed during the current study are not publicly available due Confidentiality Agreement for Public Institutions but are available from the corresponding author on reasonable request.
References
Abbasimehr H, Paki R (2022) Improving time series forecasting using LSTM and attention models. J Ambient Intell Hum Comput 13:673–691
Bhowmik RD, Sankarasubramanian A (2019) Limitations of univariate linear bias correction in yielding cross-correlation between monthly precipitation and temperature. Int J Climatol 39(11):4479–4496
Bloemheuvel S, van den Hoogen J, Jozinović D, Michelini A, Atzmueller M (2022) Graph neural networks for multivariate time series regression with application to seismic data. Int J Data Sci Anal. https://doi.org/10.1007/s41060-022-00349-6. Published 30 August 2022
Cao W, Li H, Li Q (2022) A method of thermal error prediction modeling for CNC machine tool spindle system based on linear correlation. Int J Adv Manuf Technol 118:3079–3090. https://doi.org/10.1007/s00170-021-08165-1
Chen YY, Liu HT, Hsieh HL (2019a) Time series interval forecast using GM(1,1) and NGBM(1, 1) models. Soft Comput 23:1541–1555. https://doi.org/10.1007/s00500-017-2876-0
Chen LZ, And Moschitti A, AdaRNN (2019b) Transfer learning for sequence labeling using source model and target data. Lingzhen Chen and Alessandro, pp 6260–6267. 33
Chen Y, Zheng QH, Ji SG, Tian F, Zhu HP, Liu M (2020) Identifying at-risk students based on the phased prediction model. Knowl Inf Syst 62:987–1003. https://doi.org/10.1007/s10115-019-01374-x
Chortatos A, Terragni L, Henjum S, Henjum S, Gjertsen M, Torheim LE, Gebremariam MK (2018) Consumption habits of school canteen and non-canteen users among Norwegian young adolescents. Mixed Method Anal 18(1):328. https://doi.org/10.1186/s12887-018-1299-0
Gangi LD, Lapucci M, Schoen F, Sortino A (2019) An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series. Comput Optim Appl 74:919–948
Gul MJJ, Firmansyah MH, Rho S, Paul A, BI-LSTM-LSTM (2021) Based Time Series Electricity Consumption Forecast for South Korea. In: Arabnia HR, Ferens K, de la Fuente D, Kozerenko EB, Varela O, Tinetti JA, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham, pp 897–902. https://doi.org/10.1007/978-3-030-70296-0_71
Huang TW, Jiao F (2017) Data transfer and extension for mining big meteorological data. In: Proceedings of International Conference on Intelligent Computing, Liverpool, UK, 57–66 (2017). https://doi.org/10.1007/978-3-319-63309-1_6
Huang TW, Jiao F (2020) Study on data transfer in meteorological Forecast of small and medium-sized cities and its application in zhaoqing city. Comput J 63(7):1076–1083. https://doi.org/10.1093/comjnl/bxz087
Huang TW, Jiao F, Wu ZF (2024) A precipitation forecast method based on transfer learning and long short term memory. Torrential Rain Disasters 43(1):45–53. https://doi.org/10.12406/byzh.2023-118
Jiang LW, Quan HY, Xie T, Qian JB (2022) Fish recognition in complex underwater scenes based on targeted sample transfer learning. Multimed Tools Appl 81:25303–25317. https://doi.org/10.1007/s11042-022-12525-8
Jiao F, Huang TW (2018) Research on development and application of remote control system for multimedia classroom based on cloud computing. Educ Inform Technol 24(2):1–11. https://doi.org/10.1007/s10639-018-9847-7
Khamparia A, Gupta D, de Albuquerque VHC, Sangaiah AK, Jhaveri RH (2020) Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning. J Supercomputing 76:8590–8606. https://doi.org/10.1007/s11227-020-03159-4
Lau ET, Sun L, Yang Q (2019) Modelling, prediction and classification of student academic performance using artificial neural networks. SN Appl Sci 1:982. https://doi.org/10.1007/s42452-019-0884-7
Liang GP, Fu WL, Wang KF (2019) Analysis of t-test misuses and SPSS operations in medical research papers. Burns Trauma 7(1):31
Livieris IE, Pintelas E, Pintelas P (2020) A CNN–LSTM model for gold price time-series forecasting. Neural Comput Applic 32:17351–17360
Sun SH, Zhou MH (2019) Analysis of farmers’ land transfer willingness and satisfaction based on SPSS analysis of computer software. Cluster Comput 22(S4):9123–9131
Swathi T, Kasiviswanath N, Rao AA (2022) An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Appl Intell 52:13675–13688. https://doi.org/10.1007/s10489-022-03175-2
Tilahun LA, Sekeroglu B (2020) An intelligent and personalized course advising model for higher educational institutes. SN Appl Sci 2. https://doi.org/10.1007/s42452-020-03440-4
Wen KY, Zhao GT, He BS, Ma J (2020) An improved transfer learning based time series prediction method for the high-speed rail short-term volume. Syst Eng 38(3):73–83
Yu Y, Xia X, Lang B, Liu H, PT-LSTM (2021) Extending LSTM for efficient Processing Time attributes in Time Series Prediction. In: U LH, Spaniol M, Sakurai Y, Chen J (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science, vol 12858. Springer, Cham, pp 450–464. https://doi.org/10.1007/978-3-030-85896-4_35
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The research work of this paper was supported by Science and technology research project of Guangdong Meteorological Bureau (Grant No.GRMC2020M21).
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Jiao, F., Huang, T. Analysis and forecast of college student canteen consumption based on TL-LSTM. J. of Data, Inf. and Manag. (2024). https://doi.org/10.1007/s42488-024-00122-3
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DOI: https://doi.org/10.1007/s42488-024-00122-3