smooth: Smooth package

In smooth: Forecasting Using State Space Models

Smooth package

Description

Package contains functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.

Details

Package:	smooth
Type:	Package
Date:	2016-01-27 - Inf
License:	GPL-2

The following functions are included in the package:

• es - Exponential Smoothing in Single Source of Errors State Space form.

• ces - Complex Exponential Smoothing.

• gum - Generalised Exponential Smoothing.

• ssarima - SARIMA in state space framework.

• auto.ces - Automatic selection between seasonal and non-seasonal CES.

• auto.ssarima - Automatic selection of ARIMA orders.

• sma - Simple Moving Average in state space form.

• smoothCombine - the function that combines forecasts from es(), ces(), gum(), ssarima() and sma() functions.

• cma - Centered Moving Average. This is for smoothing time series, not for forecasting.

• sim.es - simulate time series using ETS as a model.

• sim.ces - simulate time series using CES as a model.

• sim.ssarima - simulate time series using SARIMA as a model.

• sim.gum - simulate time series using GUM as a model.

• sim.sma - simulate time series using SMA.

• sim.oes - simulate time series based on occurrence part of ETS model.

• oes - occurrence part of the intermittent state space model.

There are also several methods implemented in the package for the classes "smooth" and "smooth.sim":

• orders - extracts orders of the fitted model.

• lags - extracts lags of the fitted model.

• modelType - extracts type of the fitted model.

• forecast - produces forecast using provided model.

• multicov - returns covariance matrix of multiple steps ahead forecast errors.

• pls - returns Prediction Likelihood Score.

• nparam - returns number of the estimated parameters.

• fitted - extracts fitted values from provided model.

• getResponse - returns actual values from the provided model.

• residuals - extracts residuals of provided model.

• plot - plots either states of the model or produced forecast (depending on what object is passed).

• simulate - uses sim functions (sim.es, sim.ces, sim.ssarima, sim.gum, sim.sma and sim.oes) in order to simulate data using the provided object.

• summary - provides summary of the object.

• AICc, BICc - return, guess what...

Author(s)

Ivan Svetunkov

Maintainer: Ivan Svetunkov <[e-mail]>

References

• Snyder, R. D., 1985. Recursive Estimation of Dynamic Linear Models. Journal of the Royal Statistical Society, Series B (Methodological) 47 (2), 272-276.

• Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-540-71918-2")}.

• Svetunkov Ivan and Boylan John E. (2017). Multiplicative State-Space Models for Intermittent Time Series. Working Paper of Department of Management Science, Lancaster University, 2017:4 , 1-43.

• Teunter R., Syntetos A., Babai Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214, 606-615.

• Croston, J. (1972) Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289-303.

• Syntetos, A., Boylan J. (2005) The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303-314.

• Svetunkov, I., Kourentzes, N. (February 2015). Complex exponential smoothing. Working Paper of Department of Management Science, Lancaster University 2015:1, 1-31.

• Svetunkov I., Kourentzes N. (2017) Complex Exponential Smoothing for Time Series Forecasting. Not yet published.

• Svetunkov I. (2023) Smooth forecasting with the smooth package in R. arXiv:2301.01790. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2301.01790")}.

• Svetunkov I. (2015 - Inf) "smooth" package for R - series of posts about the underlying models and how to use them: https://openforecast.org/category/r-en/smooth/.

• Kolassa, S. (2011) Combining exponential smoothing forecasts using Akaike weights. International Journal of Forecasting, 27, pp 238 - 251.

• Taylor, J.W. and Bunn, D.W. (1999) A Quantile Regression Approach to Generating Prediction Intervals. Management Science, Vol 45, No 2, pp 225-237.

• Lichtendahl Kenneth C., Jr., Grushka-Cockayne Yael, Winkler Robert L., (2013) Is It Better to Average Probabilities or Quantiles? Management Science 59(7):1594-1611. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1287/mnsc.1120.1667")}

See Also

forecast, es, ssarima, ces, gum

Examples

y <- ts(rnorm(100,10,3),frequency=12)

adam(y,h=20,holdout=TRUE)
es(y,h=20,holdout=TRUE)
gum(y,h=20,holdout=TRUE)
auto.ces(y,h=20,holdout=TRUE)
auto.ssarima(y,h=20,holdout=TRUE)

smooth documentation built on Oct. 1, 2024, 5:07 p.m.