Package contains functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
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.
ves - Vector Exponential Smoothing.
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.
iss - intermittent data state space model. This function models the part with data occurrences using one of three methods.
viss - Does the same as iss, but for the multivariate models.
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.
covar - 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 in order to simulate data using the provided object.
summary - provides summary of the object.
AICc, BICc - return, guess what...
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. http://dx.doi.org/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. (2015 - Inf) "smooth" package for R - series of posts about the underlying models and how to use them: https://forecasting.svetunkov.ru/en/tag/smooth/.
Svetunkov I. (2017). Statistical models underlying functions of 'smooth' package for R. Working Paper of Department of Management Science, Lancaster University 2017:1, 1-52.
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: [10.1287/mnsc.1120.1667](https://doi.org/10.1287/mnsc.1120.1667)
# NOT RUN {
# }
# NOT RUN {
y <- ts(rnorm(100,10,3),frequency=12)
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)
# }
# NOT RUN {
# }
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