Forecasting For Silver Closing Price and Modifying Predictions by Using Wavelet Transformation
DOI:
https://doi.org/10.25156/ptjhss.v3n1y2022.pp129-142Keywords:
Time Series Analysis, ARIMA model, Forecasting, wavelet transformationAbstract
Silver is a precious metal and the spot price not only reflects the current supply and demand condition but it also reflects investors’ expectations of future inflation and other general business/economic conditions. Therefore, the main objective of this study is to forecasting yearly silver closing price and modifying the prediction by using wavelet transformation. This study aims to analyze the time series of yearly silver closing price for the period between (1969 to 2022) using time series analysis which is (Box-Jenkins) method for the accuracy and flexibility it has in addition modifying the prediction by using wavelet transformation. In this study. The study found that the fit and efficient model according to smallest measurements (RMSE, MAPE, MAE, and ME) is the ARIMA(2, 2, 1) model. According to the results of ARIMA(2, 2, 1), the amounts of yearly silver closing price have been modified by wavelet transformation which has smallest RMSE and ME when compared with the original ARIMA(2, 2, 1).
Downloads
References
ADHISTYA ERNA PERMANASARI, INDRIANA HIDAYAH AND ISNA ALFI BUSTONI (2013), “SARIMA (SEASONAL ARIMA) IMPLEMENTATION ON TIME SERIES TO FORECAST THE NUMBER OF MALARIA INCIDENCE” CONFERENCE PAPER• CONFERENCE: CONFERENCE: INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING (ICITEE), INTERNATIONAL CONFERENCE.
AGYEMANG, BOAKYE. (2012), AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) INTERVENTION ANALYSIS MODEL FOR THE MAJOR CRIMES IN GHANA. (THE CASE OF THE EASTERN REGION), BSC, DEPARTMENT OF MATHEMATICS, KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, GHANA.
AKAIKE. A NEW LOOK AT THE STATISTICAL MODEL IDENTI_CATION. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 19(6):716{723, 1974.
AMERICAN SILVER EAGLE. THE UNITED STATES MINT. ARCHIVED FROM THE ORIGINAL ON DECEMBER 2, 2013. RETRIEVED NOVEMBER 24, 2013.
BILJANA PETREVSKA AND GOCE DELCEV (2017), “PREDICTING TOURISM DEMAND BY A.R.I.M.A. MODELS”, ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA, ISSN: 1331-677X (PRINT) 1848-9664 (ONLINE) JOURNAL HOMEPAGE: HTTP://WWW.TANDFONLINE.COM/LOI/RERO20, 2017
BOX, G. E. P., JENKINS, G. M., (1970), TIME SERIES ANALYSIS, FORECASTING AND CONTROL, 3RD ED. PRENTICE HALL, ENGLEWOOD CLIFFS, NEW JERSEY.
CHARLES, AKINMUTMI. (2011) , A SEASONAL ARIMA MODELLING OF RESIDENTIAL COAL CONSUMPTION SERIES IN NIGERIA, DEPARTMENT OF MATHEMATICS, AHMADU BELLO UNIVERSITY, NIGERIA.
CHATFIELD, C. (1996), THE ANALYSIS OF TIME SERIES, 5TH ED., CHAPMAN & HALL, NEW YORK.
COCHRANE, J. H. (2005). TIME SERIES FOR MACROECONOMICS AND FINANCE. CHICAGO: GRADUATE SCHOOL OF BUSINESS, UNIVERSITY OF CHICAGO.
CRYER, J. D, CHAN, K.(2008), TIME SERIES ANALYSIS, SECOND EDITION, SPRINGER SCIENCE BUSINESS MEDIA, NEW YORK.
DANIEL ENI AND FOLA J. ADEYEYE (2015), “SEASONAL ARIMA MODELING AND FORECASTING OF RAINFALL IN WARRI TOWN, NIGERIA”, JOURNAL OF GEOSCIENCE AND ENVIRONMENT PROTECTION, 3, 91-98 PUBLISHED ONLINE IN SCIRES.
DTREG. (2015). TIME SERIES ANALYSIS. RETRIEVED MARCH, FROM DTREG: HTTPS://WWW.DTREG.COM/METHODOLOGY.
ELANGBAM HARIDEV SINGH, 2013, “FORECASTING TOURIST INFLOW IN BHUTAN USING SEASONAL ARIMA”, INTERNATIONAL JOURNAL OF SCIENCE AND RESEARCH (IJSR), INDIA ONLINE ISSN: 2319-7064 , VOLUME 2 ISSUE 9.
ETTE H. ETUK (21 AUG 2014), “AN ADDITIVE SEASONAL BOX-JENKINS MODEL FOR NIGERIAN MONTHLY SAVINGS DEPOSIT RATES”, CONFERENCE PAPER ON (18-APR 2014).
FAISAL, F. (2001) 'FORECASTING BANGLADESH'S INFLATION USING TIME SERIES ARIMA MODELS'
GYASI-AGYEI , KWAME. (JUNE,2012), ANALYSIS AND MODELING OF PREVALENCE OF MEASLES IN THE ASHNTI REGION OF CHANA, KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, WEST AFRICA.
HAMILTON, J. D., (1994), TIME SERIES ANALYSIS, PRINCETON UNIVERSITY PRESS UNITED STATE OF AMERICA.
HUDU, MUHAMMED. (2009), INTERRUPTED TIME SERIES ANALYSIS OF THE RATE OF INFLATION IN GHANA, MSC, DEPARTMENT OF MATHEMATICS, KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, WEST AFRICA.
HURVICH, C.M., AND TSAI, C.L (1989). "REGRESSION AND TIME SERIES MODEL SELECTION IN SMALL SAMPLE", BIOMETRIKA, 76, 297-307.
JOHN A. D. ASTON, DAVID F. FINDLEY, TUCKER S. MCELROY , KELLIE C. WILLS AND DONALD E. K. MARTIN (2007), “NEW ARIMA MODELS FOR SEASONAL TIME SERIES AND THEIR APPLICATION TO SEASONAL ADJUSTMENT AND FORECASTING”, CONFERENCE PAPER ON (18-OCT 2007).
KIBAR, MUSTAFA ALPTEKIN. (2007), BUILDING COST INDEX FORCASTING WITH TIME SERIES ANALYSIS, MSC, CIVIL ENGINEERING DEPARTMENT, MIDDLE EAST TECHNICAL UNIVERSITY, TURKEY.
OKAFOR, C. AND SHAIBU, I. (2013) 'APPLICATION OF ARIMA MODELS TO NIGERIAN INFLATION DYNAMICS', RESEARCH JOURNAL OF FINANCE AND ACCOUNTING, VOL. 4, NO. 3.
S. MAKRIDAKIS, E. SPILIOTIS, V. ASSIMAKOPOULOS, (2018), “STATISTICAL AND Machine Learning FORECASTING METHODS: CONCERNS AND WAYS FORWARD”, PLOS ONE 13 (MAR. (3)).
LAI, W., 2015. WAVELET THEORY AND ITS APPLICATIONS IN ECONOMICS AND FINANCE (DOCTORAL DISSERTATION, UNIVERSITY OF LEICESTER).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Shaima N.Yaequb; Azhy A. Aziz; Huda M. Saeed; Heshu O. Faqe
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.