eprintid: 9429
rev_number: 10
eprint_status: archive
userid: 2098
dir: disk0/00/00/94/29
datestamp: 2024-09-26 01:29:29
lastmod: 2024-09-26 01:29:29
status_changed: 2024-09-26 01:29:29
type: article
metadata_visibility: show
contact_email: repository@staff.ukdw.ac.id
creators_name: , R. Gunawan Santosa
creators_name: , Antonius Rachmat Chrismanto
creators_name: , Willy Sudiarto Raharjo
creators_name: , Yuan Lukito
creators_id: 0523116701
creators_id: 0523128101
creators_id: 0523048301
creators_id: 0505078102
title: LQ45 STOCK PRICE FORECASTING: A COMPARISON STUDY OF ARIMA(P,D,Q) AND HOLT-WINTER METHOD
ispublished: pub
subjects: QA76
subjects: T1
divisions: fak_tein
full_text_status: public
keywords: ARIMA(p,d,q); boxplot; Holt-Winter; LQ45; time-series
abstract: The  Holt-Winter  method  and  ARIMA(p,d,q)  are  two  frequently  used  forecasting  techniques.  When  using  ARIMA,  errors  are  expected  to  be  connected  with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average).  The  Holt-Winter  model  comes  in  two  forms:  Multiplicative  and  Additive  Holt-Winter.  No  one  has  ever  attempted  to  compare  combined  time  series  and  cross-section data, despite many prior studies on ARIMA and Holt-Winter.  This study will compare the Holt-Winter and ARIMA accuracy rates (p,d,q) in a combined time-series and   cross-section   dataset.   LQ45   stock   prices   are   used   because   they   track   the   performance  of  45  stocks  with  substantial  liquidity,  sizable  market  caps,  and  solid  underlying businesses. We use dataset LQ45 stocks as training data in the range 2016–2021. We use data from January - February 2022 for the testing. In terms of time series data analysis, the terms indata are used for training data and outdata for forecasting test data. Daily  stock  closing  data  is  used  in  this  case:   indata  of  1458  and  outdata  of  39.The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration using a Boxplot diagram from cross-sectional data. The Boxplot diagram shows the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock.  According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is  7,0390%,  with  a  standard  deviation  of  7,7441%;  for  multiplying  Holt-Winter,  it  is  29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter,  it  is  18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, based  on  the  Boxplot  diagram,  it  can  also  be  seen  visually  that  the  ARIMA  MAPE  (p,d,q)   is   more   focused   than   Holt-Winter.   In   addition,   in   terms   of   accuracy distribution,  it  can  be  seen  that  the  MAPE  accuracy  of  the  ARIMA  method  produces  four outliers.  Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.
date: 2024-07-11
publication: International Journal of Information Technology and Computer Science Applications (IJITCSA)
volume: 2
number: 2
publisher: International Journal of Information Technology and Computer Science Applications (IJITCSA) Sekretariat Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)
pagerange: 115-129
refereed: TRUE
issn: 2964-3139
official_url: https://ejurnal.jejaringppm.org/index.php/jitcsa/issue/view/16
funders: anton@ti.ukdw.ac.id
citation:   R. Gunawan Santosa and Antonius Rachmat Chrismanto and Willy Sudiarto Raharjo and Yuan Lukito  (2024) LQ45 STOCK PRICE FORECASTING: A COMPARISON STUDY OF ARIMA(P,D,Q) AND HOLT-WINTER METHOD.  International Journal of Information Technology and Computer Science Applications (IJITCSA), 2 (2).  pp. 115-129.  ISSN 2964-3139     
document_url: https://katalog.ukdw.ac.id/9429/1/LQ45%20Stock%20Price%20Forecasting.pdf