{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:18:08Z","timestamp":1773955088681,"version":"3.50.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100023174","name":"Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023174","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Appl. Math. Stat."],"abstract":"<jats:p>This study aimed to explore big-time series data on agricultural commodities with an autocorrelation model comprising long-term processes, seasonality, and the impact of exogenous variables. Among the agricultural commodities with a large amount of data, chili prices exemplified criteria for long-term memory, seasonality, and the impact of various factors on production as an exogenous variable. These factors included the month preceding the new year and the week before the Eid al-Fitr celebration in Indonesia. To address the factors affecting price fluctuations, the Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) model was used to manage seasonality and long-term memory effects in the big data analysis. It improved with the addition of exogenous variables called SARFIMAX (SARFIMA with exogenous variables is known as SARFIMAX). After comparing the accuracy of both models, it was discovered that the SARFIMAX performed better, indicating the influence of seasonality and previous chili prices for an extended period in conjunction with exogenous variables. The SARFIMAX model gives an improvement in model accuracy by adding the effect of exogenous variables. Consequently, this observation concerning price dynamics established the cornerstone for maintaining the sustainability of chili supply even with the big data case.<\/jats:p>","DOI":"10.3389\/fams.2024.1408381","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T05:09:44Z","timestamp":1720588184000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["The seasonal model of chili price movement with the effect of long memory and exogenous variables for improving time series model accuracy"],"prefix":"10.3389","volume":"10","author":[{"given":"Dodi","family":"Devianto","sequence":"first","affiliation":[]},{"given":"Elsa","family":"Wahyuni","sequence":"additional","affiliation":[]},{"given":"Maiyastri","family":"Maiyastri","sequence":"additional","affiliation":[]},{"given":"Mutia","family":"Yollanda","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1201\/9781351259446","volume-title":"The Analysis of Time Series: An Introduction with R","author":"Chatfield","year":"2019"},{"key":"B2","doi-asserted-by":"publisher","first-page":"108544","DOI":"10.1016\/j.asoc.2022.108544","article-title":"Advanced traffic congestion early warning system based on traffic flow forecasting and extenics evaluation","volume":"118","author":"Jiang","year":"2022","journal-title":"Appl Soft Comput"},{"key":"B3","doi-asserted-by":"publisher","first-page":"111090","DOI":"10.1016\/j.asoc.2023.111090","article-title":"Interval forecasting for wind speed using a combination model based on multiobjective artificial hummingbird algorithm","volume":"150","author":"Sun","year":"2024","journal-title":"Appl Soft Comput"},{"key":"B4","doi-asserted-by":"publisher","first-page":"117201","DOI":"10.1016\/j.eswa.2022.117201","article-title":"novel decomposition-ensemble forecasting system for dynamic dispatching of smart grid with sub-model selection and intelligent optimization","volume":"201","author":"Wang","year":"2022","journal-title":"Expert Syst Appl"},{"key":"B5","doi-asserted-by":"publisher","first-page":"119807","DOI":"10.1016\/j.jenvman.2023.119807","article-title":"Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model","volume":"351","author":"Dong","year":"2024","journal-title":"J Environ Manage"},{"key":"B6","doi-asserted-by":"publisher","first-page":"122945","DOI":"10.1016\/j.techfore.2023.122945","article-title":"Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction","volume":"198","author":"Liu","year":"2024","journal-title":"Technol Forecast Soc Change"},{"key":"B7","doi-asserted-by":"publisher","first-page":"109237","DOI":"10.1016\/j.cie.2023.109237","article-title":"Combined forecasting tool for renewable energy management in sustainable supply chains","volume":"178","author":"Sun","year":"2023","journal-title":"Comp Indust Eng"},{"key":"B8","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1002\/for.2888","article-title":"Power grid operation optimization and forecasting using a combined forecasting system","volume":"42","author":"Zhang","year":"2023","journal-title":"J Forecast"},{"key":"B9","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0304-4076(80)90092-5","article-title":"Long memory relationships and the aggregation of dynamic models","volume":"14","author":"Granger","year":"1980","journal-title":"J Econom"},{"key":"B10","doi-asserted-by":"publisher","first-page":"202","DOI":"10.32479\/ijeep.13531","article-title":"Analysis of precious metal price movements using long memory and fuzzy time series Markov chain","volume":"12","author":"Arif","year":"2022","journal-title":"Int J Energy Econ Policy"},{"key":"B11","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jspi.2020.08.004","article-title":"The Hyvarinen scoring rule in Gaussian linear time series models","volume":"212","author":"Columbu","year":"2021","journal-title":"J Stat Plan Inference"},{"key":"B12","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1111\/j.1467-9892.1983.tb00371.x","article-title":"The estimation and application of long memory time series models","volume":"4","author":"Geweke","year":"1983","journal-title":"J Time Series Anal"},{"key":"B13","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.inteco.2020.11.006","article-title":"historical initial jobless claims. 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