expsmooth
| swMATH ID: | 11122 |
| Software Authors: | Hyndman, R. J.; Koehler, Anne B.; Ord, J. Keith; Snyder, Ralph D. |
| Description: | R package expsmooth: Data Sets from ”Forecasting with Exponential Smoothing”. Data sets from the book ”Forecasting with exponential smoothing: the state space approach” by Hyndman, Koehler, Ord and Snyder (Springer, 2008). Forecasting with exponential smoothing. The state space approach. Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results for this framework are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail. |
| Homepage: | http://cran.r-project.org/web/packages/expsmooth/index.html |
| Source Code: |
 https://github.com/cran/expsmooth
|
| Dependencies: | R |
| Keywords: | time series; innovations state space models |
| Related Software: | forecast; Forecast; R; fpp2; DeepAR; GitHub; Rainbow; fda (R); LightGBM; smooth; itsmr; fpp; astsa; MASS (R); COUNT; PyTorchTS; Tensor2Tensor; FinTS; robustbase; CaterpillarSSA |
| Cited in: | 66 Documents |
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