fix cycle decomposition for stochastic start > 0

Project.toml

@@ -1,7 +1,7 @@
 name = "StateSpaceLearning"
 uuid = "971c4b7c-2c4e-4bac-8525-e842df3cde7b"
 authors = ["andreramosfc <[email protected]>"]
-version = "2.0.1"
+version = "2.0.2"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

src/models/structural_model.jl

@@ -1226,13 +1226,17 @@ function get_cycle_decomposition(
         end
 
         for t in 2:T
-            ϕ_indexes =
-                max(2, model.stochastic_start):min(t, (T - max(1, model.ϕ_threshold)))
-            cycle[t] =
-                dot(c1, [cos(λ[t]), sin(λ[t])]) + sum(
-                    ϕ_cos[i] * cos(λ[t]) + ϕ_sin[i] * sin(λ[t]) for
-                    i in eachindex(ϕ_indexes)
-                )
+            if max(2, model.stochastic_start) <= min(t, (T - max(1, model.ϕ_threshold)))
+                ϕ_indexes =
+                    max(2, model.stochastic_start):min(t, (T - max(1, model.ϕ_threshold)))
+                cycle[t] =
+                    dot(c1, [cos(λ[t]), sin(λ[t])]) + sum(
+                        ϕ_cos[i] * cos(λ[t]) + ϕ_sin[i] * sin(λ[t]) for
+                        i in eachindex(ϕ_indexes)
+                    )
+            else
+                cycle[t] = dot(c1, [cos(λ[t]), sin(λ[t])])
+            end
         end
 
     else

test/models/structural_model.jl

@@ -508,6 +508,23 @@ end
         model, model.output.components, slope
     )
     @test length(trend) == 10
+
+    model = StateSpaceLearning.StructuralModel(
+        rand(100);
+        cycle="stochastic",
+        cycle_period=3,
+        outlier=false,
+        slope="stochastic",
+        ζ_threshold=0,
+        freq_seasonal=3,
+        ω_threshold=0,
+        ϕ_threshold=0,
+        stochastic_start=30,
+    )
+    StateSpaceLearning.fit!(model)
+    @test length(
+        StateSpaceLearning.get_cycle_decomposition(model, model.output.components, 3)
+    ) == 100
 end
 
 @testset "Function: simulate_states" begin