Spatiotemporal Clustering of Key Food Commodity Prices Using Multivariate Time Series

Dhiya Ulayya  Tsabitah; Yenni  Angraini; I Made Sumertajaya

doi:10.59395/ijadis.v6i3.1422

Authors

Dhiya Ulayya Tsabitah IPB University
Yenni Angraini IPB University
I Made Sumertajaya IPB University

DOI:

https://doi.org/10.59395/ijadis.v6i3.1422

Keywords:

DTW-based K-Means, Exchange rate, Food commodity prices, Hybrid VECMX-LSTM, Multivariate clustering time-series

Abstract

Food price stabilization remains a critical challenge in economic development planning and food security, particularly in developing countries like Indonesia, which exhibit high spatial and temporal diversity. To develop an efficient and adaptive predictive approach for understanding food commodity price dynamics, this study integrates multivariate time series clustering using a Dynamic Time Warping-based K-Means algorithm with a hybrid forecasting model that combines Vector Error Correction Model with Exogenous Variables and Long Short-Term Memory. The clustering evaluation results indicate reasonably cohesive group structures, with a silhouette score of 0.45 and a Davies-Bouldin Index of 0.67. Each cluster profile reveals significant differences in price trends, volatility, and anomaly patterns. Model validation using the Wilcoxon signed-rank test shows that the differences between cluster-level forecasts and individual-level actual values are generally statistically insignificant. These findings suggest that the proposed integrative approach can accurately capture regional price patterns and serve as a foundation for more data-driven and responsive policymaking in food price stabilization efforts. The 30-period forecasts for rice, eggs, and red onions reflected dynamic variations aligned with spatial characteristics: rice shows relatively stable behavior, eggs exhibit strong seasonal patterns, and red onions display the highest price volatility.

1193 644

Downloads

Download data is not yet available.

References

[1] I. Marina, D. Sukmawati, E. Juliana, and Z. N. Safa, Dinamika Pasar Komoditas Pangan Strategis: Analisis Fluktuasi Harga Dan Produksi, Paspalum: Jurnal Ilmiah Pertanian, vol. 12, no. 1, p. 160, Apr. 2024, doi: 10.35138/paspalum.v12i1.700. DOI: https://doi.org/10.35138/paspalum.v12i1.700

[2] M. S. Hermawan, L. Judijanto, S. Y. Kusumastuti, T. K. Purnamaningrum, A. Suparyati, A. Pracoyo, and A. F. N. Ilma, Pengantar ekonomi Indonesia: Perkembangan dan tantangan perekonomian dalam rangka percepatan pembangunan nasional menuju Indonesia Emas 2045. Jakarta: PT. Sonpedia Publishing Indonesia, 2025.

[3] M. Ulinnuha, F. M Afendi, and I. M. Sumertajaya, Study of Clustering Time Series Forecasting Model for Provincial Grouping in Indonesia Based on Rice Price, Indonesian Journal of Statistics and Its Applications, vol. 6, no. 1, pp. 5062, May 2022, doi: 10.29244/ijsa.v6i1p50-62. DOI: https://doi.org/10.29244/ijsa.v6i1p50-62

[4] T. Warren Liao, Clustering of time series dataa survey, Pattern Recognition, vol. 38, no. 11, pp. 18571874, Nov. 2005, doi: 10.1016/J.PATCOG.2005.01.025. DOI: https://doi.org/10.1016/j.patcog.2005.01.025

[5] N. Magfirah, "Perbandingan K-means dan K-medoids pada time series-based clustering dengan dynamic time warping pada data jumlah penduduk miskin di Sulawesi Selatan tahun 20102023", 2024. [Online]. Available: http://repository.unhas.ac.id/id/eprint/38871

[6] A. C. Caliwag and W. Lim, Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications, IEEE Access, vol. 7, pp. 5968059689, 2019, doi: 10.1109/ACCESS.2019.2914188. DOI: https://doi.org/10.1109/ACCESS.2019.2914188

[7] D. Perdana and A. Muklason, Machine Learning untuk Peramalan Kualitas Indeks Standar Pencemar Udara DKI Jakarta dengan Metode Hibrid ARIMAX-LSTM, ILKOMNIKA, vol. 5, no. 3, pp. 209222, Dec. 2023, doi: 10.28926/ILKOMNIKA.V5I3.588. DOI: https://doi.org/10.28926/ilkomnika.v5i3.588

[8] J. Sansana et al., Recent trends on hybrid modeling for Industry 4.0, Computers & Chemical Engineering, vol. 151, p. 107365, Aug. 2021, doi: 10.1016/J.COMPCHEMENG.2021.107365. DOI: https://doi.org/10.1016/j.compchemeng.2021.107365

[9] I. M. Sumertajaya, A. Wigena, K. Sadik, and E. Rohaeti, Vector Autoregressive-Moving Average Imputation Algorithm for Handling Missing Data in Multivariate Time Series, Article in IAENG International Journal of Computer Science, Accessed: Jul. 01, 2025. [Online]. Available: https://www.researchgate.net/publication/371322705

[10] N. Andayani, B. N. Ruchjana, M. N. Aidi, and I. Made Sumertajaya, Comparison ARIMA-X and VARMA-X Model to Space Time Data: A Case Study of Rice Price in Six Provinces on Java Island, 2017. [Online]. Available: www.ceserp.com/cp-jour

[11] N. Isnaini, "Analisis pengaruh harga komoditas bahan pangan terhadap inflasi di Indonesia tahun 20102016", 2018. [Online]. Available: https://digilib.uin-suka.ac.id/id/eprint/30062

[12] G. Ghaly Ghiffary et al., COMPARATIVE PERFORMANCE OF SARIMAX AND LSTM MODEL IN PREDICTING IMPORT QUANTITIES OF MILK, BUTTER, AND EGGS, BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 19, no. 1, pp. 407418, Jan. 2025, doi: 10.30598/BAREKENGVOL19ISS1PP407-418. DOI: https://doi.org/10.30598/barekengvol19iss1pp407-418

[13] D. Ulayya Tsabitah, Y. Angraini, and I. M. Sumertajaya, Implementation of Clustering Time Series with DTW to Clustering and Forecasting Rice Prices Each Provinces in Indonesia, Inferensi, vol. 8, no. 1, pp. 1323, Mar. 2025, doi: 10.12962/J27213862.V8I1.21952. DOI: https://doi.org/10.12962/j27213862.v8i1.21952

[14] J. Lee, D. Kim, and S. Sim, Temporal Multi-Features Representation Learning-Based Clustering for Time-Series Data, IEEEA, vol. 12, pp. 8767587690, 2024, doi: 10.1109/ACCESS.2024.3417348. DOI: https://doi.org/10.1109/ACCESS.2024.3417348

[15] F. Sains and D. Teknologi, Rancang Model Algoritma Long Short Term Memory Dengan Metode Rolling Windows Cross Validation: Studi Kasus Nilai Tukar Rupiah Terhadap Dollar Amerika, Aug. 2023, Accessed: Jul. 04, 2025. [Online]. Available: https://repository.uinjkt.ac.id/dspace/handle/123456789/74144

[16] A. Khoddami, E. Tamburini, D. Moore, and G. Castaldelli, Global Comparison and Future Trends of Major Food Proteins: Can Shellfish Contribute to Sustainable Food Security?, Foods 2025, Vol. 14, Page 2205, vol. 14, no. 13, p. 2205, Jun. 2025, doi: 10.3390/FOODS14132205. DOI: https://doi.org/10.3390/foods14132205

[17] A. Rencher, Methods of multivariate analysis (2nd ed.). New York: Wiley Publishing, 2005.

[18] L. Kauffman and P. J. Rousseeuw, Finding groups in data: An introduction to cluster analysis. New York: Wiley Publishing, 1990. DOI: https://doi.org/10.1002/9780470316801

[19] J. B. Kruskal, An Overview of Sequence Comparison: Time Warps, String Edits, and Macromolecules, https://doi.org/10.1137/1025045, vol. 25, no. 2, pp. 201237, Feb. 2012, doi: 10.1137/1025045. DOI: https://doi.org/10.1137/1025045

[20] R. J. Kate, Using dynamic time warping distances as features for improved time series classification, Data Mining and Knowledge Discovery, vol. 30, no. 2, pp. 283312, Mar. 2016, doi: 10.1007/S10618-015-0418-X/METRICS. DOI: https://doi.org/10.1007/s10618-015-0418-x

[21] D. F. Ristianti and J. Purwadi, Implementasi Metode VECM (Vector Error Corection Model) dalam Menganalisis Pengaruh Kurs Mata Uang, Inflasi dan Suku Bunga terhadap Jakarta Islamic Indeks (JII), Jurnal Ilmiah Matematika, vol. 6, no. 1, p. 13, Apr. 2019, doi: 10.26555/KONVERGENSI.V6I1.19544. DOI: https://doi.org/10.26555/konvergensi.v6i1.19544

[22] R. Afaq, Z. Zeeshan, Dr. S. Zaman, M. ur Rehman, and M. Shoaib, Decoding the Link between Renewable Energy and CO2 Emissions: A VECM Analysis of Growth, Energy Consumption, and Population Influences, Social Science Review Archives, vol. 3, no. 2, pp. 914928, May 2025, doi: 10.70670/SRA.V3I2.682. DOI: https://doi.org/10.70670/sra.v3i2.682

[23] B. Seo, Statistical inference on cointegration rank in error correction models with stationary covariates, Journal of Econometrics, vol. 85, no. 2, pp. 339385, Aug. 1998, doi: 10.1016/S0304-4076(97)00105-X. DOI: https://doi.org/10.1016/S0304-4076(97)00105-X

[24] R. Law, G. Li, D. K. C. Fong, and X. Han, Tourism demand forecasting: A deep learning approach, Annals of Tourism Research, vol. 75, pp. 410423, Mar. 2019, doi: 10.1016/J.ANNALS.2019.01.014. DOI: https://doi.org/10.1016/j.annals.2019.01.014

[25] H. Yadav and A. Thakkar, NOA-LSTM: An efficient LSTM cell architecture for time series forecasting, Expert Systems with Applications, vol. 238, p. 122333, Mar. 2024, doi: 10.1016/J.ESWA.2023.122333 DOI: https://doi.org/10.1016/j.eswa.2023.122333