UComp: UComp

In UComp: Automatic Unobserved Components and Other Time Series Models

UComp

Description

A package for fast automatic identification of Time series models of several kinds

Details

UComp is a package for time series modelling and forecasting of times series models inspired on different sources: 1. the structural Unobserved Components models due to A.C. Harvey (Basic Structural Model: BSM), enhanced with automatic identification tools by Diego J. Pedregal. 2. ExponenTial Smoothing by R.J. Hyndman and colaborators. The package is designed for automatic identification among a wide range of possible models for trends, cycles, seasonal and irregular components. The models may include exogenous variables. ARMA irregular components and automatic detection of outliers are also possible.

References

Harvey AC (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cam- bridge University Press.

de Jong, P & Penzer, J (1998). Diagnosing Shocks in Time Series, Journal of the American Statistical Association, 93, 442, 796-806.

Pedregal, DJ, & Young, PC (2002). Statistical approaches to modelling and forecasting time series. In M. Clements, & D. Hendry (Eds.), Companion to economic forecasting (pp. 69–104). Oxford: Blackwell Publishers.

Durbin J, Koopman SJ (2012). Time Series Analysis by State Space Methods. 38. Oxford University Press.

Proietti T and Luati A (2013). Maximum likelihood estimation of time series models: the Kalman filter and beyond, in Handbook of research methods and applications in empirical macroeconomics, ed. Nigar Hashimzade and Michael Thornton, E. Elgar, UK.

Hyndman RJ, Koehler AB, Ord JK and Snyder RD (2008), Forecasting with exponential smoothing, The State Sapce approach, Berlin, Springer-Verlag.

Maintainer

Diego J. Pedregal

Author(s)

Diego J. Pedregal

UComp documentation built on May 31, 2023, 7:41 p.m.