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.
Accuracy - returns vector of error measures for the provided forecasts and holdout.
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. (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 {
# }
Run the code above in your browser using DataLab