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  1. Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
  2. Електронний архів Науково-технічної бібліотеки
  3. Матеріали конференцій
  4. Data stream mining and processing : proceedings of the IEEE second international conference
  5. Data stream mining and processing : proceedings of the IEEE second international conference

putin IS MURDERER

Please use this identifier to cite or link to this item: https://oldena.lpnu.ua/handle/ntb/52493
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dc.contributor.authorAmbach, Daniel
dc.contributor.authorAmbach, Oleksandra
dc.coverage.temporal21-25 August 2018, Lviv
dc.date.accessioned2020-06-19T12:05:14Z-
dc.date.available2020-06-19T12:05:14Z-
dc.date.created2018-02-28
dc.date.issued2018-02-28
dc.identifier.citationAmbach D. Forecasting the Oil Price with a Periodic Regression ARFIMA-GARCH Process / Daniel Ambach, Oleksandra Ambach // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 212–217. — (Dynamic Data Mining & Data Stream Mining).
dc.identifier.isbn© Національний університет „Львівська політехніка“, 2018
dc.identifier.isbn© Національний університет „Львівська політехніка“, 2018
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/52493-
dc.description.abstractThis article provides a new periodic time series model to predict the oil price. Moreover, the approach discusses short-term forecasting of the oil price. Hence, we discuss the model fit and the out-of-sample performance. Finally, we derive further enhancements and improvements for further research.
dc.format.extent212-217
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofData stream mining and processing : proceedings of the IEEE second international conference, 2018
dc.relation.urihttp://ena.lp.edu.ua
dc.subjectlong-memory
dc.subjectforecasting
dc.subjectoil-price
dc.subjectARFIMA
dc.subjectperiodic model
dc.titleForecasting the Oil Price with a Periodic Regression ARFIMA-GARCH Process
dc.typeConference Abstract
dc.rights.holder© Національний університет “Львівська політехніка”, 2018
dc.contributor.affiliationDepartment for Data Science smava GmbH Berlin
dc.contributor.affiliationFirmenCenter Grundung und Nachfolge Berliner Sparkasse
dc.format.pages6
dc.identifier.citationenAmbach D. Forecasting the Oil Price with a Periodic Regression ARFIMA-GARCH Process / Daniel Ambach, Oleksandra Ambach // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 212–217. — (Dynamic Data Mining & Data Stream Mining).
dc.relation.references[1] Akaike, H. (1974), A new look at the statistical model identification, Automatic Control, IEEE Transactions on, 19(6), pp. 716–723.
dc.relation.references[2] Baillie, R.T. (1996), Long memory processes and fractional integration in econometrics, Journal of econometrics, 73(1), pp. 5–59.
dc.relation.references[3] Baillie, R.T., Chung, C.F., and Tieslau, M.A. (1996), Analysing inflation by the fractionally integrated ARFIMA–GARCH model, Journal of applied econometrics, pp. 23–40. 216 Lviv Polytechnic National University Institutional Repository http://ena.lp.edu.ua
dc.relation.references[4] Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, 31(3), pp. 307–327.
dc.relation.references[5] Box, G.E. and Pierce, D.A. (1970), Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American statistical Association, 65(332), pp. 1509–1526.
dc.relation.references[6] Breusch, T.S. and Pagan, A.R. (1979), A simple test for heteroscedasticity and random coefficient variation, Econometrica: Journal of the Econometric Society, pp. 1287–1294.
dc.relation.references[7] Brockwell, P.J. and Davis, R.A. (2009), Time series: theory and methods, Springer, New York.
dc.relation.references[8] Brockwell, P.J. and Davis, R.A. (2013), Time series: theory and methods, Springer Science & Business Media.
dc.relation.references[9] Durbin, J. and Watson, G.S. (1951), Testing for serial correlation in least squares regression. II, Biometrika, 38(1/2), pp. 159–177.
dc.relation.references[10] Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, pp. 987–1007.
dc.relation.references[11] Fahrmeir, L., Kneib, T., and Lang, S. (2007a), Regression: Modelle, Methoden und Anwendungen, Springer-Verlag.
dc.relation.references[12] Fahrmeir, L., Kunstler, R., Pigeot, I., and Tutz, G. (2007b), ¨ Statistik: Der Weg zur Datenanalyse, Springer-Verlag.
dc.relation.references[13] Fisher, T.J. and Gallagher, C.M. (2012), New weighted portmanteau statistics for time series goodness of fit testing, Journal of the American Statistical Association, 107(498), pp. 777–787.
dc.relation.references[14] Goldfeld, S.M. and Quandt, R.E. (1965), Some tests for homoscedasticity, Journal of the American statistical Association, 60(310), pp. 539–547.
dc.relation.references[15] Greenwald, B.C., Stiglitz, J.E., and Weiss, A. (1984), Informational imperfections in the capital market and macro-economic fluctuations.
dc.relation.references[16] Haslett, J. and Raftery, A.E. (1989), Space-time modelling with longmemory dependence: Assessing Ireland’s wind power resource, Applied Statistics, 30(1), pp. 1–50.
dc.relation.references[17] Ishida, I., Watanabe, T., et al. (2009), Modeling and Forecasting the Volatility of the Nikkei 225 realized Volatility using the ARFIMAGARCH model, Global COE Hi-Stat Discussion Paper, 32.
dc.relation.references[18] Kalkman, J., Pfeiffer, W., and Pereira, S. (2013), Are we running out of oil?
dc.relation.references[19] Kane, I.L. and Yusof, F. (2013), Assessment of Risk of Rainfall Events with a Hybrid of ARFIMA-GARCH, Modern Applied Science, 7(12), p. 78.
dc.relation.references[20] Koopman, S.J., Ooms, M., and Carnero, M.A. (2007), Periodic seasonal Reg-ARFIMA–GARCH models for daily electricity spot prices, Journal of the American Statistical Association, 102(477), pp. 16–27.
dc.relation.references[21] Leite, A., Rocha, A., and Silva, M. (2009), Long memory and volatility in HRV: an ARFIMA-GARCH approach, Computers in Cardiology, 2009, IEEE, pp. 165–168.
dc.relation.references[22] Mandelbrot, B.B. and Van Ness, J.W. (1968), Fractional Brownian motions, fractional noises and applications, SIAM review, 10(4), pp. 422–437.
dc.relation.references[23] Palm, F.C. (1996), 7 GARCH models of volatility, Handbook of statistics, 14, pp. 209–240.
dc.relation.references[24] Schwarz, G. et al. (1978), Estimating the dimension of a model, The annals of statistics, 6(2), pp. 461–464.
dc.relation.references[25] Shapiro, S.S. and Wilk, M.B. (1965), An analysis of variance test for normality (complete samples), Biometrika, 52(3/4), pp. 591–611.
dc.relation.references[26] Shumway, R.H. and Stoffer, D.S. (2010), Time series analysis and its applications: with R examples, Springer Science & Business Media.
dc.relation.references[27] White, H. (1980), A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica: Journal of the Econometric Society, pp. 817–838.
dc.relation.referencesen[1] Akaike, H. (1974), A new look at the statistical model identification, Automatic Control, IEEE Transactions on, 19(6), pp. 716–723.
dc.relation.referencesen[2] Baillie, R.T. (1996), Long memory processes and fractional integration in econometrics, Journal of econometrics, 73(1), pp. 5–59.
dc.relation.referencesen[3] Baillie, R.T., Chung, C.F., and Tieslau, M.A. (1996), Analysing inflation by the fractionally integrated ARFIMA–GARCH model, Journal of applied econometrics, pp. 23–40. 216 Lviv Polytechnic National University Institutional Repository http://ena.lp.edu.ua
dc.relation.referencesen[4] Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, 31(3), pp. 307–327.
dc.relation.referencesen[5] Box, G.E. and Pierce, D.A. (1970), Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American statistical Association, 65(332), pp. 1509–1526.
dc.relation.referencesen[6] Breusch, T.S. and Pagan, A.R. (1979), A simple test for heteroscedasticity and random coefficient variation, Econometrica: Journal of the Econometric Society, pp. 1287–1294.
dc.relation.referencesen[7] Brockwell, P.J. and Davis, R.A. (2009), Time series: theory and methods, Springer, New York.
dc.relation.referencesen[8] Brockwell, P.J. and Davis, R.A. (2013), Time series: theory and methods, Springer Science & Business Media.
dc.relation.referencesen[9] Durbin, J. and Watson, G.S. (1951), Testing for serial correlation in least squares regression. II, Biometrika, 38(1/2), pp. 159–177.
dc.relation.referencesen[10] Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, pp. 987–1007.
dc.relation.referencesen[11] Fahrmeir, L., Kneib, T., and Lang, S. (2007a), Regression: Modelle, Methoden und Anwendungen, Springer-Verlag.
dc.relation.referencesen[12] Fahrmeir, L., Kunstler, R., Pigeot, I., and Tutz, G. (2007b), ¨ Statistik: Der Weg zur Datenanalyse, Springer-Verlag.
dc.relation.referencesen[13] Fisher, T.J. and Gallagher, C.M. (2012), New weighted portmanteau statistics for time series goodness of fit testing, Journal of the American Statistical Association, 107(498), pp. 777–787.
dc.relation.referencesen[14] Goldfeld, S.M. and Quandt, R.E. (1965), Some tests for homoscedasticity, Journal of the American statistical Association, 60(310), pp. 539–547.
dc.relation.referencesen[15] Greenwald, B.C., Stiglitz, J.E., and Weiss, A. (1984), Informational imperfections in the capital market and macro-economic fluctuations.
dc.relation.referencesen[16] Haslett, J. and Raftery, A.E. (1989), Space-time modelling with longmemory dependence: Assessing Ireland’s wind power resource, Applied Statistics, 30(1), pp. 1–50.
dc.relation.referencesen[17] Ishida, I., Watanabe, T., et al. (2009), Modeling and Forecasting the Volatility of the Nikkei 225 realized Volatility using the ARFIMAGARCH model, Global COE Hi-Stat Discussion Paper, 32.
dc.relation.referencesen[18] Kalkman, J., Pfeiffer, W., and Pereira, S. (2013), Are we running out of oil?
dc.relation.referencesen[19] Kane, I.L. and Yusof, F. (2013), Assessment of Risk of Rainfall Events with a Hybrid of ARFIMA-GARCH, Modern Applied Science, 7(12), p. 78.
dc.relation.referencesen[20] Koopman, S.J., Ooms, M., and Carnero, M.A. (2007), Periodic seasonal Reg-ARFIMA–GARCH models for daily electricity spot prices, Journal of the American Statistical Association, 102(477), pp. 16–27.
dc.relation.referencesen[21] Leite, A., Rocha, A., and Silva, M. (2009), Long memory and volatility in HRV: an ARFIMA-GARCH approach, Computers in Cardiology, 2009, IEEE, pp. 165–168.
dc.relation.referencesen[22] Mandelbrot, B.B. and Van Ness, J.W. (1968), Fractional Brownian motions, fractional noises and applications, SIAM review, 10(4), pp. 422–437.
dc.relation.referencesen[23] Palm, F.C. (1996), 7 GARCH models of volatility, Handbook of statistics, 14, pp. 209–240.
dc.relation.referencesen[24] Schwarz, G. et al. (1978), Estimating the dimension of a model, The annals of statistics, 6(2), pp. 461–464.
dc.relation.referencesen[25] Shapiro, S.S. and Wilk, M.B. (1965), An analysis of variance test for normality (complete samples), Biometrika, 52(3/4), pp. 591–611.
dc.relation.referencesen[26] Shumway, R.H. and Stoffer, D.S. (2010), Time series analysis and its applications: with R examples, Springer Science & Business Media.
dc.relation.referencesen[27] White, H. (1980), A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica: Journal of the Econometric Society, pp. 817–838.
dc.citation.conferenceIEEE second international conference "Data stream mining and processing"
dc.citation.spage212
dc.citation.epage217
dc.coverage.placenameЛьвів
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