itsmr
itsmr: Time series analysis package for students. This package provides a subset of the functionality found in the Windows-based program ITSM. The intended audience is students using the textbook ”Introduction to Time Series and Forecasting” by Peter J. Brockwell and Richard A. Davis.
Keywords for this software
References in zbMATH (referenced in 213 articles , 1 standard article )
Showing results 1 to 20 of 213.
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- Baek, Changryong; Gates, Katheleen M.; Leinwand, Benjamin; Pipiras, Vladas: Two sample tests for high-dimensional autocovariances (2021)
- Beran, Jan; Steffens, Britta; Ghosh, Sucharita: Testing for the expected number of exceedances in strongly dependent seasonal time series (2021)
- Cui, Yan; Levine, Michael; Zhou, Zhou: Estimation and inference of time-varying auto-covariance under complex trend: a difference-based approach (2021)
- DelSole, Timothy; Tippett, Michael K.: Correcting the corrected AIC (2021)
- Fry, John; Griguta, Vlad-Marius; Gerber, Luciano; Slater-Petty, Helen; Crockett, Keeley: Modelling corporate bank accounts (2021)
- Furmańczyk, Konrad: Estimation of autocovariance matrices for high dimensional linear processes (2021)
- Golosnoy, Vasyl; Seifert, Miriam Isabel: Monitoring mean changes in persistent multivariate time series (2021)
- Gómez, Andrés: Outlier detection in time series via mixed-integer conic quadratic optimization (2021)
- Hančová, Martina; Gajdoš, Andrej; Hanč, Jozef; Vozáriková, Gabriela: Estimating variances in time series kriging using convex optimization and empirical BLUPs (2021)
- Honda, Hirotada: Reservoir computing with an inertial form (2021)
- Lin, Yu-Ting; Malik, John; Wu, Hau-Tieng: Wave-shape oscillatory model for nonstationary periodic time series analysis (2021)
- Monteiro, Andreia; Menezes, Raquel; Silva, Maria Eduarda: Modelling informative time points: an evolutionary process approach (2021)
- Muszkieta, Monika; Janczura, Joanna; Weron, Aleksander: Simulation and tracking of fractional particles motion. From microscopy video to statistical analysis. A Brownian bridge approach (2021)
- Phanthuna, Piyatida; Areepong, Yupaporn; Sukparungsee, Saowanit: Detection capability of the modified EWMA chart for the trend stationary AR(1) model (2021)
- Pourzanjani, Arya A.; Jiang, Richard M.; Mitchell, Brian; Atzberger, Paul J.; Petzold, Linda R.: Bayesian inference over the Stiefel manifold via the Givens representation (2021)
- Song, Xinyu; Kim, Donggyu; Yuan, Huiling; Cui, Xiangyu; Lu, Zhiping; Zhou, Yong; Wang, Yazhen: Volatility analysis with realized GARCH-Itô models (2021)
- Sourisseau, Matt; Wang, Yu Guang; Womersley, Robert S.; Wu, Hau-Tieng; Yu, Wei-Hsuan: Improve concentration of frequency and time (ConceFT) by novel complex spherical designs (2021)
- Wendling, Kyle P.; Ly, Cheng: Statistical analysis of decoding performances of diverse populations of neurons (2021)
- Boxma, Onno; Löpker, Andreas; Mandjes, Michel: On two classes of reflected autoregressive processes (2020)
- Duchesne, Pierre; Lafaye de Micheaux, Pierre; Tagne Tatsinkou, Joseph François: On strong consistency and asymptotic normality of one-step Gauss-Newton estimators in ARMA time series models (2020)