Papers by Yeasmin Khandakar
Journal of Statistical Software, 2008
Automatic forecasts of large numbers of univariate time series are often needed in business and o... more Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.
Automatic forecasts of large numbers of univariate time series are often needed in business and o... more Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and nonseasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.
Nephrology Dialysis Transplantation, Jan 10, 2017
The obesity paradox andmortality associated with surrogates of body size andmusclemass in patient... more The obesity paradox andmortality associated with surrogates of body size andmusclemass in patients receiving hemodialysis. Mayo Clin Proc 2010; 85: 991-1001 22. Kalantar-Zadeh K, Streja E, Molnar MZ et al. Mortality prediction by surrogates of body composition: an examination of the obesity paradox in hemodialysis patients using composite ranking score analysis.
Abstract: Automatic forecasts of large numbers of univariate time series are often needed in busi... more Abstract: Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and nonseasonal data, and are compared and illustrated using four real time ...
Journal of …, 2008
Automatic forecasts of large numbers of univariate time series are often needed in business and o... more Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and nonseasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.
Uploads
Papers by Yeasmin Khandakar