blue format

Author: andre_ramos

ext/PlotsExt.jl

@@ -8,7 +8,7 @@ function StateSpaceLearning.plot_point_forecast(y::Vector, prediction::Vector)
     T = length(y)
     steps_ahead = length(prediction)
     p = plot(collect(1:T), y; w=2, color="Black", lab="Historical", legend=:outerbottom)
-    plot!(collect(T+1:T+steps_ahead), prediction; lab="Forecast", w=2, color="blue")
+    plot!(collect((T + 1):(T + steps_ahead)), prediction; lab="Forecast", w=2, color="blue")
     return p
 end
 
@@ -17,10 +17,18 @@ function StateSpaceLearning.plot_scenarios(y::Vector, simulation::Matrix)
     steps_ahead, n_scenarios = size(simulation)
     p = plot(collect(1:T), y; w=2, color="Black", lab="Historical", legend=:outerbottom)
     for s in 1:(n_scenarios - 1)
-        plot!(collect(T+1:T+steps_ahead), simulation[:, s]; lab="", α=0.1, color="red")
+        plot!(
+            collect((T + 1):(T + steps_ahead)), simulation[:, s]; lab="", α=0.1, color="red"
+        )
     end
-    plot!(collect(T+1:T+steps_ahead), simulation[:, n_scenarios]; lab="Scenarios Paths", α=0.1, color="red")
+    plot!(
+        collect((T + 1):(T + steps_ahead)),
+        simulation[:, n_scenarios];
+        lab="Scenarios Paths",
+        α=0.1,
+        color="red",
+    )
     return p
 end
 
-end
+end

src/estimation_procedure.jl

@@ -326,7 +326,10 @@ function estimation_procedure(
     for key in keys(components_indexes)
         if key != "initial_states" && key != "μ1"
             component = components_indexes[key]
-            if key != "Exogenous_X" && key != "o" && !(key in ["ν1"]) && !(occursin("γ", key))
+            if key != "Exogenous_X" &&
+                key != "o" &&
+                !(key in ["ν1"]) &&
+                !(occursin("γ", key))
                 κ = count(i -> i != 0, coefs[component]) < 1 ? 0 : std(coefs[component])
                 if hasintercept
                     ts_penalty_factor[component .- 1] .= (1 / (κ + ϵ))

src/models/structural_model.jl

@@ -58,7 +58,7 @@ mutable struct StructuralModel <: StateSpaceLearningModel
     stochastic_trend::Bool
     seasonal::Bool
     stochastic_seasonal::Bool
-    freq_seasonal::Union{Int, Vector{Int}}
+    freq_seasonal::Union{Int,Vector{Int}}
     outlier::Bool
     ζ_ω_threshold::Int
     n_exogenous::Int
@@ -72,7 +72,7 @@ mutable struct StructuralModel <: StateSpaceLearningModel
         stochastic_trend::Bool=true,
         seasonal::Bool=true,
         stochastic_seasonal::Bool=true,
-        freq_seasonal::Union{Int, Vector{Int}}=12,
+        freq_seasonal::Union{Int,Vector{Int}}=12,
         outlier::Bool=true,
         ζ_ω_threshold::Int=12,
         Exogenous_X::Matrix=if typeof(y) <: Vector
@@ -261,7 +261,12 @@ end
 
 """
 function create_initial_states_Matrix(
-    T::Int, freq_seasonal::Union{Int, Vector{Int}}, steps_ahead::Int, level::Bool, trend::Bool, seasonal::Bool
+    T::Int,
+    freq_seasonal::Union{Int,Vector{Int}},
+    steps_ahead::Int,
+    level::Bool,
+    trend::Bool,
+    seasonal::Bool,
 )::Matrix
     initial_states_matrix = zeros(T + steps_ahead, 0)
     if level
@@ -332,7 +337,7 @@ function create_X(
     stochastic_trend::Bool,
     seasonal::Bool,
     stochastic_seasonal::Bool,
-    freq_seasonal::Union{Int, Vector{Int}},
+    freq_seasonal::Union{Int,Vector{Int}},
     outlier::Bool,
     ζ_ω_threshold::Int,
     Exogenous_X::Matrix{Fl},
@@ -435,9 +440,7 @@ function get_components_indexes(model::StructuralModel)::Dict
     if model.stochastic_seasonal
         for s in model.freq_seasonal
             ω_s_indexes = collect(
-                (FINAL_INDEX + 1):(FINAL_INDEX + ω_size(
-                    T, s, model.ζ_ω_threshold
-                )),
+                (FINAL_INDEX + 1):(FINAL_INDEX + ω_size(T, s, model.ζ_ω_threshold))
             )
             FINAL_INDEX += length(ω_s_indexes)
             push!(ω_indexes, ω_s_indexes)

test/models/structural_model.jl

@@ -360,7 +360,7 @@ end
         stochastic_trend=true,
         seasonal=true,
         stochastic_seasonal=true,
-        freq_seasonal=[2,5],
+        freq_seasonal=[2, 5],
         outlier=true,
         ζ_ω_threshold=0,
         Exogenous_X=Exogenous_X2,
@@ -394,7 +394,9 @@ end
 
     models = [Basic_Structural, Basic_Structural2, Local_Level, Local_Linear_Trend]
 
-    params_vec = [["ξ", "ζ", "ω_2", "ε"], ["ξ", "ζ", "ω_2", "ω_5", "ε"], ["ξ", "ε"], ["ξ", "ζ", "ε"]]
+    params_vec = [
+        ["ξ", "ζ", "ω_2", "ε"], ["ξ", "ζ", "ω_2", "ω_5", "ε"], ["ξ", "ε"], ["ξ", "ζ", "ε"]
+    ]
 
     for idx in eachindex(models)
         model = models[idx]
@@ -467,15 +469,17 @@ end
         stochastic_trend=false,
         seasonal=true,
         stochastic_seasonal=true,
-        freq_seasonal=[2,5],
+        freq_seasonal=[2, 5],
         outlier=true,
         ζ_ω_threshold=0,
         Exogenous_X=zeros(10, 0),
     )
 
     models = [model1, model2, model3, model4]
 
-    keys_vec = [["ξ", "ζ", "ω_2"], ["ζ", "ω_2"], ["ξ", "ω_2"], ["ξ", "ζ"], ["ξ", "ω_2", "ω_5"]]
+    keys_vec = [
+        ["ξ", "ζ", "ω_2"], ["ζ", "ω_2"], ["ξ", "ω_2"], ["ξ", "ζ"], ["ξ", "ω_2", "ω_5"]
+    ]
 
     for idx in eachindex(models)
         model = models[idx]