The application of Neural Prophet Time Series in predicting rice stock at Rice Stores
DOI:
10.47709/cnahpc.v5i2.2725Keywords:
Accurate forecasting, Model Performance, Neural Prophet, Rice, Time SeriesDimension Badge Record
Abstract
Efficient inventory management and consistent rice supply are pivotal for the sustainability of small-scale food stalls. This research introduces an innovative approach to address this challenge through the Neural Prophet algorithm. By synergizing neural networks with additive regression models, the Neural Prophet captures intricate temporal patterns and trends within rice sales data. Our study evaluates the Neural Prophet's effectiveness in predicting rice sales, specifically for essential food vendors. Leveraging historical sales data from June 2022 to April 2023, the algorithm incorporates seasonality and trends and integrates external events, such as holidays, to heighten prediction precision. Our findings underscore the Neural Prophet's remarkable prowess in forecasting rice sales at primary food kiosks, adeptly discerning data trends and fluctuations, culminating in reliable future sales projections. The model boasts compelling performance metrics: MAE = 12.90, RMSE = 15.80, and Loss = 0.0313. Beyond its technical merits, this research carries significant practical implications, empowering proprietors and suppliers of basic food stalls to streamline inventory management, avert stockouts, and curtail overstocking by harnessing the precision of rice demand forecasting facilitated by the Neural Prophet algorithm.
Downloads
Abstract viewed = 354 times
References
ArunKumar, K. E., Kalaga, D. V., Sai Kumar, C. M., Chilkoor, G., Kawaji, M., & Brenza, T. M. (2021). Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Averag. Applied Soft Computing, 103(December 2019), 107161. https://doi.org/10.1016/j.asoc.2021.107161
Borges, D., & Nascimento, M. C. V. (2022). COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach. Applied Soft Computing, 125, 109181. https://doi.org/10.1016/j.asoc.2022.109181
Costa, R. L. de C. (2022). Convolutional-LSTM networks and generalization in forecasting of household photovoltaic generation. Engineering Applications of Artificial Intelligence, 116(September), 105458. https://doi.org/10.1016/j.engappai.2022.105458
Hindarto, D., & Handri Santoso. (2021). Android APK Identification using Non Neural Network and Neural Network Classifier. Journal of Computer Science and Informatics Engineering (J-Cosine), 5(2), 149–157. https://doi.org/10.29303/jcosine.v5i2.420
Hindarto, D., & Santoso, H. (2022). PERFORMANCE COMPARISON OF SUPERVISED LEARNING USING NON-NEURAL NETWORK AND NEURAL NETWORK. Janapati, 11, 49–62.
Karthick, M. K., Kiruthiga, G., Ms Saraswathi, P., Dhiyanesh, B., & Radha, R. (2022). A Subset Scaling Recursive Feature Collection Based DDoS Detection Using Behavioural Based Ideal Neural Network For Security In A Cloud Environment. Procedia Computer Science, 215, 509–518. https://doi.org/10.1016/j.procs.2022.12.053
K?l?ç, D. K., & U?ur, Ö. (2023). Hybrid wavelet-neural network models for time series. Applied Soft Computing, 144, 110469. https://doi.org/10.1016/j.asoc.2023.110469
Mao, Q., Zhang, K., Yan, W., & Cheng, C. (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public Health, 11(5), 707–712. https://doi.org/10.1016/j.jiph.2018.04.009
Munim, Z. H., Fiskin, C. S., Nepal, B., & Chowdhury, M. M. H. (2023). Forecasting container throughput of major Asian ports using the Prophet and hybrid time series models. Asian Journal of Shipping and Logistics, xxxx, 1–11. https://doi.org/10.1016/j.ajsl.2023.02.004
Qiu, K., Li, J., & Chen, D. (2022). Optimized long short-term memory (LSTM) network for performance prediction in unconventional reservoirs. Energy Reports, 8, 15436–15445. https://doi.org/10.1016/j.egyr.2022.11.130
Saeed, N., Nguyen, S., Cullinane, K., Gekara, V., & Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method. Transport Policy, 133(December 2022), 86–107. https://doi.org/10.1016/j.tranpol.2023.01.012
Saputra, A. D., Hindarto, D., & Santoso, H. (2023). Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron, 8(1), 48–55. https://doi.org/10.33395/sinkron.v8i1.11906
Wijaya, S., Heryadi, Y., Arifin, Y., Suparta, W., & Lukas. (2023). Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control. Procedia Computer Science, 216(2022), 213–220. https://doi.org/10.1016/j.procs.2022.12.129
Wu, F., Wang, Y., Liu, Y., Liu, Y., & Zhang, Y. (2021). Simulated responses of global rice trade to variations in yield under climate change: Evidence from main rice-producing countries. Journal of Cleaner Production, 281, 124690. https://doi.org/10.1016/J.JCLEPRO.2020.124690
Zhang, B., Song, C., Li, Y., & Jiang, X. (2022). Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model. Heliyon, 8(11), e11670. https://doi.org/10.1016/j.heliyon.2022.e11670
ZHOU, Q., YUAN, R., ZHANG, W. yang, GU, J. fei, LIU, L. jun, ZHANG, H., WANG, Z. qin, & YANG, J. chang. (2023). Grain yield, nitrogen use efficiency and physiological performance of indica/japonica hybrid rice in response to various nitrogen rates. Journal of Integrative Agriculture, 22(1), 63–79. https://doi.org/10.1016/j.jia.2022.08.076
Downloads
ARTICLE Published HISTORY
Accepted Date: 2023-08-15
Published Date: 2023-08-15
How to Cite
Issue
Section
License
Copyright (c) 2023 Djarot Hindarto, Ferial Hendrata, Mochamad Hariadi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
