Fix bug in Naive assertion and increase coverage

Author: guilhermebodin

src/models/naive_models.jl

@@ -12,7 +12,7 @@ typeof_model_elements(model::NaiveModel) = eltype(model.y)
 
 function assert_zero_missing_values(model::NaiveModel)
     for i in 1:length(model.y)
-        if isnan(y[i])
+        if isnan(model.y[i])
             return error("model $(typeof(model)) does not support missing values.)")
         end
     end
@@ -44,6 +44,7 @@ mutable struct Naive <: NaiveModel
 end
 
 function fit!(model::Naive)
+    assert_zero_missing_values(model)
     residuals = model.y[2:end] - model.y[1:end-1]
     model.residuals = residuals
     model.sigma2 = var(residuals)
@@ -111,6 +112,7 @@ mutable struct SeasonalNaive <: NaiveModel
 end
 
 function fit!(model::SeasonalNaive)
+    assert_zero_missing_values(model)
     residuals = model.y[model.seasonal+1:end] - model.y[1:end-model.seasonal]
     model.residuals = residuals
     model.sigma2 = var(residuals)
@@ -185,6 +187,7 @@ mutable struct ExperimentalSeasonalNaive <: NaiveModel
 end
 
 function fit!(model::ExperimentalSeasonalNaive)
+    assert_zero_missing_values(model)
     residuals = model.y[model.seasonal+1:end] - model.y[1:end-model.seasonal]
     model.residuals = residuals
     model.sigma2 = var(residuals)

test/models/naive_models.jl

@@ -3,11 +3,13 @@
 
     model = Naive(nile.flow)
     fit!(model)
+    StateSpaceModels.get_standard_residuals(model)
     forec = forecast(model, 10; bootstrap = true)
     forec = forecast(model, 10)
     scenarios = simulate_scenarios(model, 10, 1_000)
     @test monotone_forecast_variance(forec)
     test_scenarios_adequacy_with_forecast(forec, scenarios; rtol = 1e-1)
+    cross_validation(model, 10, 70; n_scenarios=100)
 
     air_passengers = CSV.File(StateSpaceModels.AIR_PASSENGERS) |> DataFrame
     log_air_passengers = log.(air_passengers.passengers)
@@ -18,10 +20,19 @@
     forec = forecast(model, 60)
     scenarios = simulate_scenarios(model, 60, 1_000)
     @test monotone_forecast_variance(forec)
+    StateSpaceModels.reinstantiate(model, model.y)
 
     # Just see if it runs
     model = ExperimentalSeasonalNaive(log_air_passengers, 12)
     fit!(model)
     forec = forecast(model, 60)
     scenarios = simulate_scenarios(model, 60, 1_000)
+    StateSpaceModels.reinstantiate(model, model.y)
+
+    using StateSpaceModels
+    using Test
+    y = randn(100)
+    y[10] = NaN
+    model = Naive(y)
+    @test_throws ErrorException fit!(model)
 end

test/visualization/components.jl

@@ -20,4 +20,24 @@
     @test length(rec) == 3
     rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, ks)
     @test length(rec) == 3
+
+    model = ExponentialSmoothing(log_air_passengers; trend = true, seasonal = 12)
+    fit!(model)
+    kf = kalman_filter(model)
+    ks = kalman_smoother(model)
+
+    rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, kf)
+    @test length(rec) == 4
+    rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, ks)
+    @test length(rec) == 4
+
+    finland_fatalities = CSV.File(StateSpaceModels.VEHICLE_FATALITIES) |> DataFrame
+    log_finland_fatalities = log.(finland_fatalities.ff)
+    model = ExponentialSmoothing(log_finland_fatalities; trend = true)
+    fit!(model)
+
+    rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, kf)
+    @test length(rec) == 3
+    rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, ks)
+    @test length(rec) == 3
 end

test/visualization/forecast.jl

@@ -11,4 +11,15 @@
     scen = simulate_scenarios(model, 12, 100)
     rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, scen)
     @test length(rec) == 2
+
+    model = SeasonalNaive(log_air_passengers, 12)
+    fit!(model)
+    # forecasting
+    forec = forecast(model, 12)
+    rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, forec)
+    @test length(rec) == 4
+    # simulating
+    scen = simulate_scenarios(model, 12, 100)
+    rec = RecipesBase.apply_recipe(Dict{Symbol, Any}(), model, scen)
+    @test length(rec) == 2
 end