ac

The application of Neural Prophet Time Series in predicting rice stock at Rice Stores

Authors

  • Djarot Hindarto Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta, Indonesia
  • Ferial Hendrata Prodi Sistem Informasi, Universitas Narotama, Surabaya, Indonesia
  • Mochamad Hariadi Department of Electrical Engineering, Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

DOI:

10.47709/cnahpc.v5i2.2725

Keywords:

Accurate forecasting, Model Performance, Neural Prophet, Rice, Time Series

Dimension 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

Download data is not yet available.
Google Scholar Cite Analysis
Abstract viewed = 235 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

Submitted Date: 2023-08-15
Accepted Date: 2023-08-15
Published Date: 2023-08-15

How to Cite

Hindarto, D., Hendrata, F. ., & Hariadi, M. . (2023). The application of Neural Prophet Time Series in predicting rice stock at Rice Stores. Journal of Computer Networks, Architecture and High Performance Computing, 5(2), 668-681. https://doi.org/10.47709/cnahpc.v5i2.2725

Most read articles by the same author(s)

1 2 > >>