Time Series Analysis: With Applications in R

By J. Cryer, Kung-Sik Chan. 2010

818 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bc6000eec49d4c20bbc602fee605877cf59ef5b3

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Citations in the corpus

Performance of state space and ARIMA models for consumer retail sales forecasting (2015, methodology): “We investigated the required transformations for variance stabilization and decided to take logarithms in the case of Boots, Booties and Flats data (for more details see Cryer and Chan [26]).”
Statsmodels: Econometric and Statistical Modeling with Python (2010, background): “In particular R is increasing in popularity as evidenced by the recent textbooks by Cryer and Chan (2008), Kleiber and Zeileis (2008), and Vinod (2008).”
Neglected biological patterns in the residuals (2011, background): “2008; Cryer and Chan 2008). Both Grissom and Kim (2005) and Erceg-Hurn and Mirosevich (2008) provide a more detailed discussion about why heteroscedasticity occurs in real data sets.”
Assumptions of multiple regression: Correcting two misconceptions (2013, unknown): “For example, the use of time series data may require the use of some form of time series analysis (see Cryer & Chan, 2008; Hamilton, 1994), while the analysis of nested data may require the use of a multilevel model (see Goldstein, 2011).”
Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, background): “Therefore, up to two autoregressive and moving average parameters are sufficient (Cryer and Chan 2008).”
Long-term prediction of polar motion using a combined SSA and ARMA model (2018, methodology): “The EACF indicated that the ARMA (2,8) model was suitable for the residual SSA series.”