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Sains Malaysiana 53(11)(2024): 3779-3789
http://doi.org/10.17576/jsm-2024-5311-20
Development of Food Commodity Price Forecasting Model
as an Early Warning System with a Multivariate Time Series Clustering
(Pembangunan Model Peramalan Harga Komoditi Makanan sebagai Sistem Amaran Awal dengan Pengelompokan Siri
Masa Multivariat)
I MADE SUMERTAJAYA1,*,
EMBAY ROHAETI2, ANWAR FITRIANTO1 & WINDHIARSO PONCO
ADI P3
1Department of
Statistics, Faculty of Mathematics and Science, Bogor Agricultural University,
16680 Bogor, West Java, Indonesia
Diserahkan: 17 Jun 2024/Diterima: 30 September 2024
Abstract
Fluctuations in food commodity
prices have a significant impact on a country’s food security, purchasing
power, and economic growth. Therefore, good governance is needed to maintain
price stability, one of which is by developing a forecasting model as an early
warning system. This study aims to develop a food commodity price forecasting
model using Multivariate Time Series Clustering (MTSClust)
and Vector Autoregressive Imputation Method with Moving Average (VAR-IMMA)
approaches for food commodities in the Indonesian region. The data used in this
study consisted of daily prices of 13 commodities from 103 districts/cities in
Indonesia. Data analysis was conducted in several stages, namely VAR modeling, K-means Euclidean clustering, profiling, and
forecasting. The results show that 103 sample districts/cities across Indonesia
can be grouped into four types of regions based on food price movement
patterns. There are homogeneous islands such as Maluku where the sample
district/city are in the same cluster, but there are also heterogeneous islands
such as Kalimantan and Papua with their four clusters. The forecasting
evaluation results show good accuracy with Root Mean Square Error (RMSE) scores
below IDR 1000.00 in most cases, which is equivalent to Mean Absolute
Percentage Error (MAPE) scores below 10%. However, two commodities, namely
cayenne pepper and red chili, need more attention due to relatively high RMSE
and MAPE scores, although not exceeding 30% MAPE in most cases. These results show
that the MTSClust and VAR-IMMA approaches are
accurate in forecasting food commodity prices, although further research is
needed for the two chili commodities.
Keywords: Early warning system; food security; MTSClust; VAR; VAR-IMMA
Abstrak
Turun naik dalam harga komoditi makanan mempunyai kesan yang besar terhadap keselamatan makanan, kuasa beli dan pertumbuhan ekonomi sesebuah negara. Oleh itu, tadbir urus yang baik diperlukan untuk mengekalkan kestabilan harga, salah satunya dengan membangunkan model peramalan sebagai sistem amaran awal.
Kajian ini bertujuan untuk membangunkan model peramalan harga komoditi makanan menggunakan pendekatan Siri Masa Multivariat Berkelompok (MTSClust) dan Kaedah Pengimputan Vektor Autoregresif dengan Purata Bergerak (VAR-IMMA) bagi komoditi makanan di wilayah Indonesia. Data yang digunakan dalam kajian ini terdiri daripada harga harian 13 komoditi dari 103 daerah/bandar di Indonesia. Analisis data dijalankan dalam beberapa peringkat iaitu pemodelan VAR, K-means Euclidean berkelompok, pemprofilan dan peramalan.
Hasil kajian menunjukkan bahawa 103 sampel daerah/bandar di seluruh Indonesia boleh dikumpulkan kepada empat jenis wilayah berdasarkan corak pergerakan harga makanan. Terdapat pulau homogen seperti Maluku di mana daerah/bandar sampel berada dalam kelompok yang sama, tetapi terdapat juga pulau heterogen seperti Kalimantan dan Papua dengan empat kelompoknya.
Keputusan penilaian peramalan menunjukkan ketepatan yang baik dengan skor Punca Min Ralat Kuasa Dua (RMSE) di bawah IDR 1000.00 dalam kebanyakan kes, yang bersamaan dengan skor Min Ralat Peratusan Mutlak (MAPE) di bawah 10%. Walau bagaimanapun, dua komoditi iaitu lada cayenne dan cili merah memerlukan lebih perhatian kerana markah RMSE dan MAPE yang agak tinggi, walaupun tidak melebihi 30% MAPE dalam kebanyakan kes. Keputusan ini menunjukkan bahawa pendekatan MTSClust dan VAR-IMMA adalah tepat dalam meramalkan harga komoditi makanan, walaupun kajian lanjut diperlukan untuk kedua-dua komoditi cili ini.
Kata kunci: Keselamatan makanan; MTSClust; sistem amaran awal;
VAR; VAR-IMMA
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