ITSM2000

Introduction to time series and forecasting. 3rd edition. This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user’s own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered. New to this edition: – A chapter devoted to Financial Time Series – Introductions to Brownian motion, Lévy processes and Itô calculus – An expanded section on continuous-time ARMA processes


References in zbMATH (referenced in 19 articles )

Showing results 1 to 19 of 19.
Sorted by year (citations)

  1. Hoga, Yannick: Quantifying the data-dredging bias in structural break tests (2022)
  2. Lauwers, Oliver; Vermeersch, Christof; De Moor, Bart: Cepstral identification of autoregressive systems (2022)
  3. Miguel, Sergio Brenner; Phandoidaen, Nathawut: Multiplicative deconvolution in survival analysis under dependency (2022)
  4. Palm, Bruna G.; Bayer, Fábio M.; Cintra, Renato J.: 2-D Rayleigh autoregressive moving average model for SAR image modeling (2022)
  5. Beran, Jan; Steffens, Britta; Ghosh, Sucharita: Testing for the expected number of exceedances in strongly dependent seasonal time series (2021)
  6. Cui, Yan; Levine, Michael; Zhou, Zhou: Estimation and inference of time-varying auto-covariance under complex trend: a difference-based approach (2021)
  7. Fry, John; Griguta, Vlad-Marius; Gerber, Luciano; Slater-Petty, Helen; Crockett, Keeley: Modelling corporate bank accounts (2021)
  8. Gómez, Andrés: Outlier detection in time series via mixed-integer conic quadratic optimization (2021)
  9. Hančová, Martina; Gajdoš, Andrej; Hanč, Jozef; Vozáriková, Gabriela: Estimating variances in time series kriging using convex optimization and empirical BLUPs (2021)
  10. Honda, Hirotada: Reservoir computing with an inertial form (2021)
  11. Méndez-Gordillo, Alma Rosa; Cadenas, Erasmo: Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique (2021)
  12. Mikhaylov, A. N.; Guseinov, D. V.; Belov, A. I.; Korolev, D. S.; Shishmakova, V. A.; Koryazhkina, M. N.; Filatov, D. O.; Gorshkov, O. N.; Maldonado, D.; Alonso, F. J.; Roldán, J. B.; Krichigin, A. V.; Agudov, N. V.; Dubkov, A. A.; Carollo, A.; Spagnolo, B.: Stochastic resonance in a metal-oxide memristive device (2021)
  13. Song, Xinyu; Kim, Donggyu; Yuan, Huiling; Cui, Xiangyu; Lu, Zhiping; Zhou, Yong; Wang, Yazhen: Volatility analysis with realized GARCH-Itô models (2021)
  14. Soukhovolsky, Vladislav; Kovalev, Anton; Pitt, Anne; Shulman, Katerina; Tarasova, Olga; Kessel, Boris: The cyclicity of coronavirus cases: “waves” and the “weekend effect” (2021)
  15. Dou, Xialiang; Anitescu, Mihai: Distributionally robust optimization with correlated data from vector autoregressive processes (2019)
  16. Velilla, Santiago; Nguyen, Huong: A new diagnostic tool for VARMA((p,q)) models (2019)
  17. Zhou, Bo; van den Akker, Ramon; Werker, Bas J. M.: Semiparametrically point-optimal hybrid rank tests for unit roots (2019)
  18. Grechuk, Bogdan; Zabarankin, Michael: Direct data-based decision making under uncertainty (2018)
  19. Brockwell, Peter J.; Davis, Richard A.: Introduction to time series and forecasting (2016)