Uneingeschränkter Zugang

On the Method of Identification of Atypical Observations in Time Series


Zitieren

Algur, S. P., and Biradar, J. G. (2017). Cooks distance and mahanabolis distance outlier detection methods to identify review spam. International Journal of Engineering and Computer Science, 6(6), 21638-21649. doi:10.18535/ijecs/v6i6.1610.18535/ijecs/v6i6.16Search in Google Scholar

Anderson, D. R., Sweeney, D. J., and Williams, T. A. (2011). Statistics for business and economics 11e. Mason: South-Western Cengage Learning.Search in Google Scholar

Atkinson, A. C., Koopman, S. J., and Shephard, N. (1997). Detecting shocks: Outliers and breaks in time series. Journal of Econometrics, (80), 387-422. Retrieved from https://www.sciencedirect.com/science/article/pii/S030440769700050X10.1016/S0304-4076(97)00050-XSearch in Google Scholar

Belsely, D. A., Huh, E., and Welsch, R. E. (2004). Regression diagnostics. Identifying influential data and sources of collinearity. Hoboken: John Wiley & Sons.Search in Google Scholar

Bonham, C. D. (1971). Testing for outlying observations in a sample group. Journal of Range Management, 24(4), 310-312. doi:10.2307/389695210.2307/3896952Search in Google Scholar

Chatterjee, S., and Hadi, A. S. (1986). Influential observations, high leverage points, and outliers in linear regression. Statistical Science, 1(3), 379-393. Retrieved from http://www.mathstat.ualberta.ca/~wiens/stat578/papers/Chatterjee%20&%20Hadi.pdfSearch in Google Scholar

Cook, D. R. (1977). Detection of influential observation in linear regression. Technometrics, 19(1), 15-18. doi:10.2307/126824910.2307/1268249Search in Google Scholar

Cook, D. R. (1980). Identification of outliers. Chapman & Hall. doi:10.1007/978-94-015-3994-410.1007/978-94-015-3994-4Search in Google Scholar

Cousineau D., and Chartier, S. (2010). Outliers detection and treatment: a review. International Journal of Psychological Research, 3(1), 58-67.10.21500/20112084.844Search in Google Scholar

Dittmann, P., Dittman, I., Szabela-Pasierbińska, E., and Szpulak, A. (2009). Prognozowaniu w zarządzaniu przedsiębiorstwem. Kraków: Wolters Kluwer Polska Sp. z o.o.Search in Google Scholar

Hoaglin, D. C., and Welsch, R. E. (1978). The Hat Matrix in Regression and ANOVA. The American Statistician, 32(1), 17-22. Retrieved from http://www.stat.ucla.edu/~cocteau/stat201b/handout/hat.pdfSearch in Google Scholar

Kannan Senthamarai, K., and Manoj, K., (2015). Outlier detection in multivariate data. Applied Mathematical Sciences, 47(9), 2317-2324Search in Google Scholar

Oesterreich, M. (2017). Symulacyjne badanie wpływu liczby i rozmieszczenia luk na dokładność prognoz w szeregu czasowym dla danych dziennych. Ekonometria, 1(55), 57-68. doi:10.15611/ekt.2017.1.0510.15611/ekt.2017.1.05Search in Google Scholar

Osborne, J. W., Overbay, A. (2004). The power of outliers (and why researchers should ALWAYS check for them). Practical Assessment, Research & Evaluation, 9(6). Retrieved from https://pareonline.net/getvn.asp?v=9%26n=6Search in Google Scholar

Paul, R. K. (2018). Some methods of detection of outliers in linear models. Retrieved from IASRI: http://iasri.res.in/seminar/AS-299/ebooks/2005-2006/Msc/trim1/4.%20Some%20Methods%20of%20Detection%20of%20Outliers%20in%20Linear%20Regression%20Model-Ranjit.pdfSearch in Google Scholar

Tsay, R. S. (1988). Outliers, level shifts and variance changes in time series. Journal of Forecasting, (7), 1-20. doi:10.1002/for.398007010210.1002/for.3980070102Search in Google Scholar

Watson, S. M., Clark, S., Tight, M., and Redfern, E. (1992). An influence method for outliers detection applied to time series traffic data. ITS Working Paper, (365). Retrieved from http://eprints.white-rose.ac.uk/2206/1/ITS365_WP365_uploadable.pdfSearch in Google Scholar

Watson, S. M., Tight, M., Clark, S., and Redfern, E. (1991). Detection of outliers in time series. ITS Working Paper (362). Retrieved from http://eprints.whiterose.ac.uk/2209/1/ITS261_WP362_uploadable.pdfSearch in Google Scholar

Zahari, S. M., Zainol, M. S., Sopian, K. B., Zaharim, A., and Ibrahim, K. (2010). Additive outliers (AO) and innovative outliers (IO) in GARCH (1, 1) processes. AMERICAN-MATH’10 Proceedings of the 2010 American conference on Applied mathematics, 471-479. Retrieved from https://www.researchgate.net/publication/228664831_Additive_outliers_AO_and_innovative_outliers_IO_in_GARCH_1_1_processesSearch in Google Scholar

Zawadzki, J. (1999). Ekonometryczne metody predykcji dla danych sezonowych w warunkach braku pełnej informacji. Szczecin: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego.Search in Google Scholar

Zawadzki, J. (2003). Zastosowanie hierarchicznych modeli szeregów czasowych w prognozowaniu zmiennych ekonomicznych z wahanimi sezonowymi. Szczecin: Wydawnictwo Akademii Rolniczej w Szczecinie.Search in Google Scholar

eISSN:
2449-9994
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Wirtschaftswissenschaften, Volkswirtschaft, andere, Betriebswirtschaft, Mathematik und Statistik für Ökonomen, Mathematik, Sozialwissenschaften, Soziologie, Allgemeines