StateSpace.jl: A Julia package for state space modeling. State space models are a very general type of dynamic statistical model, and have been used to estimate everything from biological populations to the position of Apollo 11 to the weather this weekend. In a nutshell, they are useful when we want to know the state of some process, but we can’t observe it directly. They have two main pieces. First is the process model, which describes probabilistically how the hidden state evolves from one time step to the next. Second is the observation model, which describes, again probabilistically, how the state is translated into the quantities we observe. These process and observation functions can be linear or nonlinear, and the process noise and observation errors may be Gaussian, or from some other probability distribution. This package aims to provide methods to perform the common prediction, filtering, and smoothing tasks for each type of model.
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References in zbMATH (referenced in 1 article )
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- Raphael Saavedra, Guilherme Bodin, Mario Souto: StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework (2019) arXiv