DOI: https://doi.org/10.14710/medstat.16.2.215-226

MODEL COMPARISON OF VECTOR AUTOREGRESSIVE RESHAPED AND SARIMA IN SEASONAL DATA (A CASE STUDY OF TEA PRODUCTION IN PT PERKEBUNAN NUSANTARA VIII INDONESIA)

*Dewi Juliah Ratnaningsih orcid scopus  -  Statistics Study Program, Universitas Terbuka, Indonesia
Fia Fridayanti Adam  -  Vocational Education Program, Universitas Indonesia, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

How to cite (APA): Ratnaningsih, D. J., & Adam, F. F. (2024). MODEL COMPARISON OF VECTOR AUTOREGRESSIVE RESHAPED AND SARIMA IN SEASONAL DATA (A CASE STUDY OF TEA PRODUCTION IN PT PERKEBUNAN NUSANTARA VIII INDONESIA). MEDIA STATISTIKA, 16(2), 215-226. https://doi.org/10.14710/medstat.16.2.215-226
Citation Format:
Abstract
PT Perkebunan Nusantara VIII (PTPN VIII) is a State-Owned Enterprise (BUMN). It operates in the plantation sector.  The leading commodity is tea.  The demand for tea produced by PTPN VIII is increasing. Thus, planning tea production is necessary. One of the production planning efforts is through forecasting based on previous data.  Tea  production data is time-series data.  It contains seasonal elements and is dependent on other locations. We will analyze data with these criteria  using space-time models, one of which is vector autoregressive (VAR). VAR models the relationship  between observations on certain variables at one time. It also models the observation of the variable itself at previous times. Additionally, VAR models  the relationship  between observations and other variables at previous times. This paper explains how to forecast tea  production. It uses the reconstituted VAR and Seasonal Autoregressive Moving Average (SARIMA) models. The results showed that the reconstituted VAR model was better than the SARIMA model in predicting tea production. The tea production prediction was at the Sedep and Santosa plantations in Bandung Regency.
Fulltext View|Download
Keywords: Tea production; time series; seasonal; spacetime; VAR reshaped

Article Metrics:

Plum Print visual indicator of research metrics
No metrics available.

Article Info
Section: Articles
Language : EN
Recent articles
MODELING OF WORLD CRUDE OIL PRICE BASED ON PULSE FUNCTION INTERVENTION ANALYSIS APPROACH HANDLING OF OVERDISPERSION CASES IN MORBIDITY DATA IN SELUMA REGENCY PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY More recent articles
Most cited articles
MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA KETAHANAN HIDUP PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION Front-Matter APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI More cited articles
  1. [Anonymous]. (2010). Indonesian Tea Statistics. Jakarta: Statistics Indonesia
  2. Choi TM, Yu Y, Au KF. (2011). A hybrid SARIMA wavelet transform method for sales forecasting. Decision Support System. 51: 130-140
  3. Etuk, E.H. (2013). The fitting of a SARIMA model to monthly Naira-Euro Exchange Rates. Mathematical Theory and Modeling. 3(1): 17-26
  4. Gijo, E. V. (2011). Demand forecasting of tea by seasonal ARIMA model. International Journal of Business Excellence. 4(1): 111-124
  5. Goswani K, Hazarika J. (2017). Monthly temperature prediction based on arima model: a case study in dibrugarh station of assam, India. International Journal of Advanced Research in Computer Science. 8(8): 292:298. ISSN No. 0976-5697. DOI: http://dx.doi.org/10.26483/ijarcs.v8i8.4590
  6. Halim S, Chandra A. (2011). Automatic multivariate time series. Journal of Industrial Engineering. 13 (1): 19-26
  7. Hussain, M.N., Ali, A. (2017). Forecasting of Pakistan’s Import Prices of Black Tea Using ANN and SARIMA Model. International Review of Management and Business Research. 6 (4): 1372-1382
  8. Mercy C, Kihoro J. (2015). Application of vector autoregressive (VAR) process in
  9. modelling reshaped seasonal univariate time series. Science Journal of Applied Mathematics and Statistics. 3(3): 124-135
  10. Mohamed N, Ahmad MH, Suhartono, Ismail Z. (2011). Improving short term load forecasting using double seasonal arima mpdel. World Applied Sciences Journal. 15(2):223-231. ISSN 1818-4952
  11. Nursodik, H., Santoso, S.I., Nurfadilah, S. (2021). Competitiveness and determining factors of Indonesian tea export volume in the world market. Habitat. 32(3): 163-172
  12. Pfeifer, P.E., and S.J. Deutsch. (1980). A three-step iterative procedure for space-time modeling. Technometrics. 22(1)
  13. Rosadi D. 2006. Learning Module: Time series Analysis. An Introduction. Departement of Statistics, Gadjah Mada University. Yogyakarta: Andi Publisher
  14. Sims CA. (1986). Macroeconomics and reality. Journal Econometrics. 48(1):38-48
  15. Suhartono. (2011). Time Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive Model. Journal of Mathematics and Statistics. 7 (1): 20-27. ISSN 1549-3644
  16. Voora, V., Bermúdez, S., & Larrea, C. (2019). Global Market Report : Tea. Sustainable commodities marketplace series 2019. International Institute For Sustainable Development (IISD), Canada. https://www.jstor.org/stable/resrep22027?seq=1#me-tadata_info_tab_contents
  17. Wei WS. (2006). Time Series Analysis: Univariate and Mulivariate Methods. Pearson Education. Boston: Pearson Addison Wesley
  18. Wutsqa DU, Suhartono. (2007). Comparative study of VAR Model and STAR on Tea Production Forecast in West Java. http://staff.uny.ac.id/sites/default-/files/132048772/Suhartono_&_Doriva_2007.pdf access on 25 Juni 2015

Last update:

No citation recorded.

Last update: 2025-04-04 09:59:56

No citation recorded.