SSpace: A Flexible and General State Space Toolbox for MATLAB

  • Diego J. Pedregal
  • C. James Taylor


This chapter illustrates the utility of, and provides the basic documentation for, SSpace, a recently developed MATLAB toolbox for the analysis of State Space systems. The key strength of the toolbox is its generality and flexibility, both in terms of the particular state space form selected and the manner in which generic models are straightforwardly translated into MATLAB code. With the help of a relatively small number of functions, it is possible to fully exploit the power of state space systems, performing operations such as filtering, smoothing, forecasting, interpolation, signal extraction and likelihood estimation. The chapter provides an overview of SSpace and demonstrates its usage with several worked examples.


State Space State Space Model State Space Form State Space System General State Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Junta de Comunidades de Castilla-La Mancha grant PII1I09-0209-6050.


  1. 1.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis Forecasting and Control, 3rd edn. Prentice Hall, Englewood Cliffs (1994) MATHGoogle Scholar
  2. 2.
    Bryson, A.E., Ho, Y.C.: Applied Optimal Control, Optimization, Estimation and Control. Blaisdell Publishing, Waltham (1969) Google Scholar
  3. 3.
    Carnero, C., Pedregal, D.J.: Modelling and forecasting occupational accidents of different severity levels in Spain. Reliab. Eng. Syst. Saf. 95, 1134–1141 (2010) CrossRefGoogle Scholar
  4. 4.
    Casals, J., Jerez, M., Sotoca, S.: Exact smoothing for stationary and non-stationary time series. Int. J. Forecast. 16, 59–69 (2000) CrossRefGoogle Scholar
  5. 5.
    Cobb, G.W.: The problem of the Nile: conditional solution to a change point problem. Biometrika 65, 243–251 (1978) MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    de Jong, P.: Smoothing and interpolation with the state space model. J. Am. Stat. Assoc. 84, 1085–1088 (1989) MATHCrossRefGoogle Scholar
  7. 7.
    Exadaktylos, V., Silva, M., Aerts, J.-M., Taylor, C.J., Berckmans, D.: Real-time recognition of sick pig cough sounds. Comput. Electron. Agric. 63, 207–214 (2008) CrossRefGoogle Scholar
  8. 8.
    Harvey, A.C.: Forecasting Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge (1989) Google Scholar
  9. 9.
    Durbin, J., Koopman, S.J.: Time Series Analysis by State Space Methods. Oxford University Press, London (2001) MATHGoogle Scholar
  10. 10.
    Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Springer, Berlin (1970) MATHGoogle Scholar
  11. 11.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME Trans., J. Basic Eng. D 83, 95–108 (1960) MathSciNetGoogle Scholar
  12. 12.
    Ng, C.N., Young, P.C.: Recursive estimation and forecasting of non-stationary time series. J. Forecast. 9, 173–204 (1990) CrossRefGoogle Scholar
  13. 13.
    Pedregal, D.J., Carnero, C.: State space models for condition monitoring. A case study. Reliab. Eng. Syst. Saf. 91, 171–180 (2006) CrossRefGoogle Scholar
  14. 14.
    Pedregal, D.J., Carnero, C.: Vibration analysis diagnostics by continuous-time models: a case study. Reliab. Eng. Syst. Saf. 94, 244–253 (2009) CrossRefGoogle Scholar
  15. 15.
    Pedregal, D.J., Contreras, J., Sánchez, A.: ECOTOOL: a general MATLAB forecasting toolbox with applications to electricity markets. In: Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A., Rebennack, S., Sorokin, A. (eds.) Handbook of Networks in Power Systems, pp. 69–104. Springer, Berlin (2010) Google Scholar
  16. 16.
    Pedregal, D.J., Dejuán, O., Gómez, N., Tobarra, M.A.: Modelling demand for crude oil products in Spain. Energy Policy 37, 4417–4427 (2009) CrossRefGoogle Scholar
  17. 17.
    Pedregal, D.J., Pérez, J.J.: Should quarterly government finance statistics be used for fiscal surveillance in Europe? Int. J. Forecast. 26, 794–807 (2010) CrossRefGoogle Scholar
  18. 18.
    Pedregal, D.J., Trapero, J.R.: Mid-term hourly electricity forecasting based on a multi-rate approach. Energy Convers. Manag. 51, 105–111 (2010) CrossRefGoogle Scholar
  19. 19.
    Pedregal, D.J., Young, P.C.: Statistical approaches to modelling and forecasting time series. In: Clements, M., Hendry, D. (eds.) Companion to Economic Forecasting. Blackwell, Oxford (2002) Google Scholar
  20. 20.
    Pedregal, D.J., Young, P.C.: Development of improved adaptive approaches to electricity demand forecasting. J. Oper. Res. Soc. 59, 1066–1076 (2008) CrossRefGoogle Scholar
  21. 21.
    Schweppe, F.: Evaluation of likelihood function for Gaussian signals. IEEE Trans. Inf. Theory 11, 61–70 (1965) MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Taylor, C.J., Chotai, A., Young, P.C.: Nonlinear control by input-output state variable feedback pole assignment. Int. J. Control 82, 1029–1044 (2009) MathSciNetMATHCrossRefGoogle Scholar
  23. 23.
    Taylor, C.J., Pedregal, D.J., Young, P.C., Tych, W.: Time series analysis and forecasting with the Captain Toolbox. Environ. Model. Softw. 22, 797–814 (2007) CrossRefGoogle Scholar
  24. 24.
    Taylor, C.J., Shaban, E.M., Stables, M.A., Ako, S.: Proportional-Integral-Plus (PIP) control applications of state dependent parameter models. IMECHE Proc., Part I, J. Syst. Control Eng. 221(17), 1019–1032 (2007) CrossRefGoogle Scholar
  25. 25.
    Tsay, R.S.: Testing and modeling threshold autoregressive processes. J. Am. Stat. Assoc. 84, 231–240 (1989) MathSciNetMATHCrossRefGoogle Scholar
  26. 26.
    Young, P.C.: Recursive Estimation and Time Series Analysis. Springer, Berlin (1984) MATHGoogle Scholar
  27. 27.
    Young, P.C., Pedregal, D.J., Tych, W.: Dyanmic harmonic regression. J. Forecast. 18, 369–394 (1999) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.E.T.S. de Ingenieros Industriales and Institute of Applied Mathematics to Science and Engineering (IMACI)University of Castilla, La ManchaCiudad RealSpain
  2. 2.Engineering DepartmentLancaster UniversityLancasterUK

Personalised recommendations