Update

Author: guilhermebodin

Project.toml

@@ -1,7 +1,7 @@
 name = "StateSpaceModels"
 uuid = "99342f36-827c-5390-97c9-d7f9ee765c78"
 authors = ["raphaelsaavedra <[email protected]>, guilhermebodin <[email protected]>, mariohsouto"]
-version = "0.5.23"
+version = "0.6.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

src/cross_validation.jl

@@ -58,7 +58,7 @@ function cross_validation(model::StateSpaceModel, steps_ahead::Int, start_idx::I
         y_to_fit = model.system.y[1:start_idx - 1 + i]
         y_to_verify = model.system.y[start_idx + i:start_idx - 1 + i + steps_ahead]
         model_to_fit = reinstantiate(model, y_to_fit)
-        fit!(model_to_fit; filter=filter, optimizer=optimizer, save_hyperparameter_distribution_results=false)
+        fit!(model_to_fit; filter=filter, optimizer=optimizer, save_hyperparameter_distribution=false)
         forec = forecast(model_to_fit, steps_ahead; filter=filter)
         scenarios = simulate_scenarios(model_to_fit, steps_ahead, n_scenarios; filter=filter)
         expected_value_vector = forecast_expected_value(forec)[:]

src/fit.jl

@@ -3,7 +3,7 @@
         model::StateSpaceModel;
         filter::KalmanFilter=default_filter(model),
         optimizer::Optimizer=Optimizer(Optim.LBFGS()),
-        save_hyperparameter_distribution_results::Bool=true
+        save_hyperparameter_distribution::Bool=true
     )
 
 Estimate the state-space model parameters via maximum likelihood. The resulting optimal
@@ -30,7 +30,7 @@ function fit!(
     model::StateSpaceModel;
     filter::KalmanFilter=default_filter(model),
     optimizer::Optimizer=default_optimizer(model),
-    save_hyperparameter_distribution_results::Bool=true
+    save_hyperparameter_distribution::Bool=true
 )
     isfitted(model) && return model
     @assert has_fit_methods(typeof(model))
@@ -48,7 +48,7 @@ function fit!(
     opt_hyperparameters = opt.minimizer
     update_model_hyperparameters!(model, opt_hyperparameters)
 
-    if save_hyperparameter_distribution_results
+    if save_hyperparameter_distribution
         numerical_hessian = Optim.hessian!(func, opt_hyperparameters)
         try 
             std_err = numerical_hessian |> pinv |> diag .|> sqrt
@@ -61,7 +61,7 @@ function fit!(
                 "change the optimization algorithm by using the kwarg fit(...; optimizer = "*
                 "Optimizer(StateSpaceModels.Optim.THE_METHOD_OF_YOUR_CHOICE()))" * 
                 "The list of possible algorithms can be found on this link https://julianlsolvers.github.io/Optim.jl/stable/#" * 
-                "Otherwise you can simply skip this proccess by using fit(...; save_hyperparameter_distribution_results=false) "
+                "Otherwise you can simply skip this proccess by using fit(...; save_hyperparameter_distribution=false) "
             )
             std_err = fill(NaN, number_hyperparameters(model))
             fill_results!(model, opt_loglikelihood, std_err)

src/models/sarima.jl

@@ -761,7 +761,7 @@ function fit_candidate_models!(candidate_models::Vector{SARIMA}, show_trace::Boo
     non_converged_models = Int[]
     for (i, model) in enumerate(candidate_models)
         try 
-            fit!(model; save_hyperparameter_distribution_results=false)
+            fit!(model; save_hyperparameter_distribution=false)
             if isnan(model.results.llk)
                 show_trace && println(model, " - diverged")
                 push!(non_converged_models, i)

test/models/linear_regression.jl

@@ -6,7 +6,7 @@
         X = rand(100, 5)
         y = rand(100)
         model = LinearRegression(X, y)
-        fit!(model; save_hyperparameter_distribution_results=false)
+        fit!(model; save_hyperparameter_distribution=false)
         β = X \ y
         for i in 1:size(X, 2)
             @test get_constrained_value(model, "β_$i") .≈ β[i] atol = 1e-5
@@ -27,7 +27,7 @@
         10.0
     ]
     model = LinearRegression(X, y)
-    fit!(model; save_hyperparameter_distribution_results=false)
+    fit!(model; save_hyperparameter_distribution=false)
     forec = forecast(model, [1 6.0])
     forec.expected_value[1] == [11.0]
 end

test/visualization/forecast.jl

@@ -2,7 +2,7 @@
     air_passengers = CSV.File(StateSpaceModels.AIR_PASSENGERS) |> DataFrame
     log_air_passengers = log.(air_passengers.passengers)
     model = BasicStructural(log_air_passengers, 12)
-    fit!(model; save_hyperparameter_distribution_results=false)
+    fit!(model; save_hyperparameter_distribution=false)
     # forecasting
     forec = forecast(model, 12)
     rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, forec)