Getting DomainError when fitting some models
Closed this issue · 2 comments
A DomainError is raised when fitting some models (please see stack trace below). I have copied a model below that triggers this error (the model is meaningless; just the simplest I could generate to show the problem).
using StateSpaceModels
value = repeat([10.0], 40)
seasonality = 7
events = [0.0 for i in 1:40, j in 1:1]
ssm = StateSpaceModels.BasicStructuralExplanatory(value, seasonality, events)
StateSpaceModels.fit!(ssm)
The error is:
DomainError with -3.5937804133234396e-10: log will only return a complex result if called with a complex argument. Try log(Complex(x)).
And the stack trace:
[1] throw_complex_domainerror(f::Symbol, x::Float64)
@ Base.Math .\math.jl:33
[2] _log(x::Float64, base::Val{:ℯ}, func::Symbol)
@ Base.Math .\special\log.jl:304
[3] log
@ .\special\log.jl:269 [inlined]
[4] update_llk!
@ C:....julia\packages\StateSpaceModels\XjBwj\src\filters\univariate_kalman_filter.jl:198 [inlined]
[5] update_kalman_state!(kalman_state::StateSpaceModels.UnivariateKalmanState{Float64}, y::Float64, Z::Vector{Float64}, T::Matrix{Float64}, H::Float64, R::Matrix{Float64}, Q::Matrix{Float64}, d::Float64, c::Vector{Float64}, skip_llk_instants::Int64, t::Int64)
@ StateSpaceModels C:....julia\packages\StateSpaceModels\XjBwj\src\filters\univariate_kalman_filter.jl:280
[6] filter_recursions!(kalman_state::StateSpaceModels.UnivariateKalmanState{Float64}, sys::LinearUnivariateTimeVariant{Float64}, steadystate_tol::Float64, skip_llk_instants::Int64)
@ StateSpaceModels C:....julia\packages\StateSpaceModels\XjBwj\src\filters\univariate_kalman_filter.jl:339
[7] optim_kalman_filter(sys::LinearUnivariateTimeVariant{Float64}, filter::UnivariateKalmanFilter{Float64})
@ StateSpaceModels C:....julia\packages\StateSpaceModels\XjBwj\src\filters\univariate_kalman_filter.jl:288
[8] optim_loglike(model::BasicStructuralExplanatory, filter::UnivariateKalmanFilter{Float64}, unconstrained_hyperparameters::Vector{Float64})
@ StateSpaceModels C:....julia\packages\StateSpaceModels\XjBwj\src\kalman_filter_and_smoother.jl:15
[9] #41
@ C:....julia\packages\StateSpaceModels\XjBwj\src\fit.jl:40 [inlined]
[10] finite_difference_gradient!(df::Vector{Float64}, f::StateSpaceModels.var"#41#42"{UnivariateKalmanFilter{Float64}, BasicStructuralExplanatory}, x::Vector{Float64}, cache::FiniteDiff.GradientCache{Nothing, Nothing, Nothing, Vector{Float64}, Val{:central}(), Float64, Val{true}()}; relstep::Float64, absstep::Float64, dir::Bool)
@ FiniteDiff C:....julia\packages\FiniteDiff\KkXlb\src\gradients.jl:275
[11] finite_difference_gradient!
@ C:....julia\packages\FiniteDiff\KkXlb\src\gradients.jl:224 [inlined]
[12] (::NLSolversBase.var"#g!#44"{StateSpaceModels.var"#41#42"{UnivariateKalmanFilter{Float64}, BasicStructuralExplanatory}, FiniteDiff.GradientCache{Nothing, Nothing, Nothing, Vector{Float64}, Val{:central}(), Float64, Val{true}()}})(storage::Vector{Float64}, x::Vector{Float64})
@ NLSolversBase C:....julia\packages\NLSolversBase\cfJrN\src\objective_types\twicedifferentiable.jl:113
[13] (::NLSolversBase.var"#fg!#45"{StateSpaceModels.var"#41#42"{UnivariateKalmanFilter{Float64}, BasicStructuralExplanatory}})(storage::Vector{Float64}, x::Vector{Float64})
@ NLSolversBase C:....julia\packages\NLSolversBase\cfJrN\src\objective_types\twicedifferentiable.jl:117
[14] value_gradient!!(obj::NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}, x::Vector{Float64})
@ NLSolversBase C:....julia\packages\NLSolversBase\cfJrN\src\interface.jl:82
[15] value_gradient!(obj::NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}, x::Vector{Float64})
@ NLSolversBase C:....julia\packages\NLSolversBase\cfJrN\src\interface.jl:69
[16] value_gradient!(obj::Optim.ManifoldObjective{NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}}, x::Vector{Float64})
@ Optim C:....julia\packages\Optim\6Lpjy\src\Manifolds.jl:50
[17] (::LineSearches.var"#ϕdϕ#6"{Optim.ManifoldObjective{NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}}, Vector{Float64}, Vector{Float64}, Vector{Float64}})(α::Float64)
@ LineSearches C:....julia\packages\LineSearches\Ki4c5\src\LineSearches.jl:84
[18] secant2!(ϕdϕ::LineSearches.var"#ϕdϕ#6"{Optim.ManifoldObjective{NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}}, Vector{Float64}, Vector{Float64}, Vector{Float64}}, alphas::Vector{Float64}, values::Vector{Float64}, slopes::Vector{Float64}, ia::Int64, ib::Int64, phi_lim::Float64, delta::Float64, sigma::Float64, display::Int64)
@ LineSearches C:....julia\packages\LineSearches\Ki4c5\src\hagerzhang.jl:368
[19] (::LineSearches.HagerZhang{Float64, Base.RefValue{Bool}})(ϕ::Function, ϕdϕ::LineSearches.var"#ϕdϕ#6"{Optim.ManifoldObjective{NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}}, Vector{Float64}, Vector{Float64}, Vector{Float64}}, c::Float64, phi_0::Float64, dphi_0::Float64)
@ LineSearches C:....julia\packages\LineSearches\Ki4c5\src\hagerzhang.jl:269
[20] HagerZhang
@ C:....julia\packages\LineSearches\Ki4c5\src\hagerzhang.jl:101 [inlined]
[21] perform_linesearch!(state::Optim.LBFGSState{Vector{Float64}, Vector{Vector{Float64}}, Vector{Vector{Float64}}, Float64, Vector{Float64}}, method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, d::Optim.ManifoldObjective{NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}})
@ Optim C:....julia\packages\Optim\6Lpjy\src\utilities\perform_linesearch.jl:59
[22] update_state!(d::NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}, state::Optim.LBFGSState{Vector{Float64}, Vector{Vector{Float64}}, Vector{Vector{Float64}}, Float64, Vector{Float64}}, method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"})
@ Optim C:....julia\packages\Optim\6Lpjy\src\multivariate\solvers\first_order\l_bfgs.jl:204
[23] optimize(d::NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}, initial_x::Vector{Float64}, method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, options::Optim.Options{Float64, Nothing}, state::Optim.LBFGSState{Vector{Float64}, Vector{Vector{Float64}}, Vector{Vector{Float64}}, Float64, Vector{Float64}})
@ Optim C:....julia\packages\Optim\6Lpjy\src\multivariate\optimize\optimize.jl:54
[24] optimize(d::NLSolversBase.TwiceDifferentiable{Float64, Vector{Float64}, Matrix{Float64}, Vector{Float64}}, initial_x::Vector{Float64}, method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, options::Optim.Options{Float64, Nothing})
@ Optim C:....julia\packages\Optim\6Lpjy\src\multivariate\optimize\optimize.jl:36
[25] fit!(model::BasicStructuralExplanatory; filter::UnivariateKalmanFilter{Float64}, optimizer::Optimizer, save_hyperparameter_distribution::Bool)
@ StateSpaceModels C:....julia\packages\StateSpaceModels\XjBwj\src\fit.jl:42
[26] fit!(model::BasicStructuralExplanatory)
@ StateSpaceModels C:....julia\packages\StateSpaceModels\XjBwj\src\fit.jl:35
[27] top-level scope
@ In[5]:8
[28] eval
@ .\boot.jl:373 [inlined]
[29] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
@ Base .\loading.jl:1196
Hi @SupplyChef the time series is constant, when there is no variance such errors could happen because when we maximize the loglikelihood we have to take the log of innovations (the prediction errors). We could add some checks to see if the time-series is constant. Otherwise, does it happens at a time-series not constant?
Thank you for looking into this. Yes, the issue seems to only happen on edge cases. It'd be great if you could add the checks.
Thank you!