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statespace 0.1.4

  • Installation
  • Quickstart
  • User guide
  • Examples
  • Reference
    • Design notes
    • Differences from statsmodels
    • References
  • GitHub
  • PyPI
  • Installation
  • Quickstart
  • User guide
  • Examples
  • Reference
  • Design notes
  • Differences from statsmodels
  • References
  • GitHub
  • PyPI

statespace#

Linear Gaussian state space models, following Part I of Durbin and Koopman, Time Series Analysis by State Space Methods (2nd ed., 2012). The core is a Fortran library; ssfortran is its Python package.

  • Installation
    • Requirements
    • Python
    • Fortran
    • Data for the examples
    • Documentation
  • Quickstart
    • Python
    • Fortran
    • Next
  • User guide
    • The state space model
    • Initialization
    • Filtering and smoothing
    • Estimation
    • Structural and ARIMA models
    • Defining models
    • Forecasting and simulation
    • Diagnostic checking
    • Performance
  • Examples
    • Road casualties and the seat belt law (DK §8.2)
    • Front and rear seat passengers (DK §8.3)
    • Box-Jenkins analysis of Internet use (DK §8.4)
    • Spline smoothing of motorcycle data (DK §8.5)
    • Dynamic factor analysis of the yield curve (DK §8.6)
    • Benchmarking (DK §3.10.2)
    • Series from two sources (DK §3.10.3)
  • Reference
    • Python API
    • Fortran API
    • C interface
  • Design notes
    • Principles and testing
    • Diffuse initialization
    • Initialization of a damped cycle
    • Output coordinates of the univariate treatment
    • Missing observations
    • The steady state
    • Estimation
    • Performance
    • Interfaces
  • Differences from statsmodels
    • Methods in both libraries
    • Only in this library
    • Only in statsmodels
    • Comparing results
  • References
    • Methods
    • Data
    • Software

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