KaydedilenlerDerginizi ekleyin

Dergiler / Gazi University Journal of Science / 2026 / Volume: 25 Issue: 2

Residual Types in Time Series and Their Applications

Mehmet Güray Ünsal
Reşat Kasap
Sayfa
—
Yayın Tarihi
2012-04-16
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Then a simulation study was conducted to show differences in decisions when different residual types were considered in diagnostic checking.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Residual types in time series has not been investigated throughly in literature.
📊
Temel Bulgular & Veri:This study aims to provide practical applications of residual types.
💡
Sonuç & Katkı:Then a simulation study was conducted to show differences in decisions when different residual types were considered in diagnostic checking.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Time Series Analysis · Residual Types · Diagnostic Checking

Abstract

Residual types in time series has not been investigated throughly in literature. This study aims to provide practical applications of residual types. In this study, firstly, basic information about different types of residuals was given and some features of the residuals were investigated with numerical applications . Then a simulation study was conducted to show differences in decisions when different residual types were considered in diagnostic checking.

🔖

Bu Makaleye Atıf Yap

Ünsal, M. G., Kasap, R. (2012). Residual Types in Time Series and Their Applications. Gazi University Journal of Science, Volume: 25 Issue: 2. https://ajindex.com/articles/1269820

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (16)

  1. Ansley, C.F., Newbold, P., On the finite sample distribution of residual autocorrelations in autoregressive moving average models. Biometrika, 66:547-553 (1979).
  2. Akdi, Y.,, Time Series Analysis (Unit Roots and Cointegration), Bıçaklar Bookstore, Ankara, 1-18 (2003).
  3. Arranz, M.A., Portmanteau Test Statistics in Time Series: Time-Oriented Language, 25-32 (2005).
  4. Battaglia, F., “Approximate power of portmanteau tests for time series”, Statistics and Probability Letters, 9: 341 (1990).
  5. Box, G.E.P., Jenkins, G.M and Reinsel, G.C., Time Series analysis: Forecasting and a Control, Prentice Hall: New Jersey, 7-88, 224-307 (1994).
  6. Brockwell, P.J., Davis, R.A., Introduction to Time Series and Forecasting. Sec ond ed. Springer, NewYork (2002).
  7. Davies, N., Triggs, C. M. And:Newbold, P., “Significance levels of the Box-Pierce “Portmanteau statistic in finite samples”, Biometrika, 64: 517-522 (1977).
  8. Kasap, R., An analysis of the Istanbul Stock Exchange (ISE) national-100 index: a statistical approach. ISE Review, 6:27-33 (1998).
  9. Ljung, G.M. and Box, G.E.P., On a measure of lack of fit in time series models. Biometrika, 65: 297-303 (1978).
  10. Mauricio, J.A., Computing and Using Residuals in Time Series Models, Computational Statistics & Data Analysis, 52, 3:1746 – 1763 (2008).
  11. Unsal, M.G., Kasap, R., Analysis Of The Matrices Used For Computing Of Residuals Types For ARMA(1,1) Model, 19. Statistics Research Symposium, TÜİK, Ankara (2010).
  12. Wei, W.W.S., Time Series Analysis: Univariate and Multivariate Methods, Addison-Wesley Publishing Company, Inc.: UK, 32-64, 135-156 (1990).
  13. Akdi, Y.,, Time Series Analysis (Unit Roots and Cointegration), Bıçaklar Bookstore, Ankara, 1-18 (2003).
  14. Arranz, M.A., Portmanteau Test Statistics in Time Series: Time-Oriented Language, 25-32 (2005).
  15. Box, G.E.P., Jenkins, G.M and Reinsel, G.C., Time Series analysis: Forecasting and a Control, Prentice Hall: New Jersey, 7-88, 224-307 (1994).
  16. Davies, N., Triggs, C. M. And:Newbold, P., “Significance levels of the Box-Pierce “Portmanteau statistic in finite samples”, Biometrika, 64: 517-522 (1977).