fix innovation_simulation phi

.github/workflows/ci.yml

@@ -27,7 +27,7 @@ jobs:
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
+      - uses: actions/cache@v3
         env:
           cache-name: cache-artifacts
         with:

Project.toml

@@ -1,7 +1,7 @@
 name = "StateSpaceLearning"
 uuid = "971c4b7c-2c4e-4bac-8525-e842df3cde7b"
 authors = ["andreramosfc <[email protected]>"]
-version = "1.4.2"
+version = "1.4.3"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

src/models/structural_model.jl

@@ -97,7 +97,7 @@ mutable struct StructuralModel <: StateSpaceLearningModel
         else
             @assert seasonal ? size(y, 1) > minimum(freq_seasonal) : true "Time series must be longer than the seasonal period"
         end
-        @assert 1 <= stochastic_start < length(y) "stochastic_start must be greater than or equal to 1"
+        @assert 1 <= stochastic_start < length(y) "stochastic_start must be greater than or equal to 1 and smaller than the length of the time series"
         @assert 0 < dumping_cycle <= 1 "dumping_cycle must be greater than 0 and less than or equal to 1"
         if cycle_period != 0 && !isempty(cycle_period)
             if typeof(cycle_period) <: Vector
@@ -249,8 +249,14 @@ o_size(T::Int, stochastic_start::Int)::Int = T - max(1, stochastic_start) + 1
     # Returns
     - `Int`: Size of ϕ calculated from T.
 """
-ϕ_size(T::Int, ζ_ω_threshold::Int, stochastic_start::Int) =
-    2 * (T - max(2, stochastic_start) + 1) - (ζ_ω_threshold * 2)
+function ϕ_size(T::Int, ζ_ω_threshold::Int, stochastic_start::Int)
+    ζ_ω_threshold = ζ_ω_threshold == 0 ? 1 : ζ_ω_threshold
+    if stochastic_start == 1
+        return (2 * (T - max(2, stochastic_start) + 1) - (ζ_ω_threshold * 2)) - 2
+    else
+        return (2 * (T - max(2, stochastic_start) + 1) - (ζ_ω_threshold * 2))
+    end
+end
 
 """
     create_ξ(T::Int, steps_ahead::Int, stochastic_start::Int)::Matrix
@@ -400,7 +406,12 @@ function create_ϕ(
         )
     end
 
-    return X[:, 1:(end - (ζ_ω_threshold * 2))]
+    ζ_ω_threshold = ζ_ω_threshold == 0 ? 1 : ζ_ω_threshold
+    if stochastic_start == 1
+        return X[:, 3:(end - (ζ_ω_threshold * 2))]
+    else
+        return X[:, 1:(end - (ζ_ω_threshold * 2))]
+    end
 end
 
 """
@@ -933,12 +944,12 @@ function get_innovation_simulation_X(
     elseif occursin("ϕ_", innovation)
         i = parse(Int, split(innovation, "_")[2])
         deterministic_cycle_matrix = create_deterministic_cycle_matrix(
-            model.cycle_matrix, length(model.y), steps_ahead
+            model.cycle_matrix, length(model.y), steps_ahead + 1
         )
         return create_ϕ(
             deterministic_cycle_matrix[i],
-            length(model.y),
-            steps_ahead,
+            length(model.y) + steps_ahead + 1,
+            0,
             model.ζ_ω_threshold,
             model.stochastic_start,
         )

test/models/structural_model.jl

@@ -99,16 +99,16 @@ end
     @test StateSpaceLearning.ω_size(10, 2, 0, 1) == 9
     @test StateSpaceLearning.ω_size(10, 2, 2, 1) == 7
     @test StateSpaceLearning.o_size(10, 1) == 10
-    @test StateSpaceLearning.ϕ_size(10, 0, 1) == 18
-    @test StateSpaceLearning.ϕ_size(10, 2, 1) == 14
+    @test StateSpaceLearning.ϕ_size(10, 0, 1) == 14
+    @test StateSpaceLearning.ϕ_size(10, 2, 1) == 12
 
     @test StateSpaceLearning.ξ_size(10, 5) == 5
     @test StateSpaceLearning.ζ_size(10, 2, 5) == 3
     @test StateSpaceLearning.ζ_size(10, 0, 5) == 5
     @test StateSpaceLearning.ω_size(10, 2, 0, 5) == 6
     @test StateSpaceLearning.ω_size(10, 2, 2, 5) == 4
     @test StateSpaceLearning.o_size(10, 6) == 5
-    @test StateSpaceLearning.ϕ_size(10, 0, 5) == 12
+    @test StateSpaceLearning.ϕ_size(10, 0, 5) == 10
 
     X_ξ1 = StateSpaceLearning.create_ξ(5, 0, 1)
     X_ξ2 = StateSpaceLearning.create_ξ(5, 2, 1)
@@ -255,11 +255,11 @@ end
         isapprox.(
             X_ϕ1,
             [
-                1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-                0.866025 0.5 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-                0.5 0.866025 0.866025 0.5 1.0 0.0 0.0 0.0 0.0 0.0
-                2.77556e-16 1.0 0.5 0.866025 0.866025 0.5 1.0 0.0 0.0 0.0
-                -0.5 0.866025 2.77556e-16 1.0 0.5 0.866025 0.866025 0.5 1.0 0.0
+                0.0 0.0 0.0 0.0 0.0 0.0
+                1.0 0.0 0.0 0.0 0.0 0.0
+                0.866025 0.5 1.0 0.0 0.0 0.0
+                0.5 0.866025 0.866025 0.5 1.0 0.0
+                2.77556e-16 1.0 0.5 0.866025 0.866025 0.5
             ],
             atol=1e-6,
         ),
@@ -842,11 +842,45 @@ end
         isapprox.(
             X4,
             [
-                1.0 0.0 0.0 0.0 0.0 0.0
-                -0.5 0.866025 1.0 0.0 0.0 0.0
-                -0.5 -0.866025 -0.5 0.866025 1.0 0.0
-                1.0 -6.10623e-16 -0.5 -0.866025 -0.5 0.866025
-                -0.5 0.866025 1.0 -6.10623e-16 -0.5 -0.866025
+                0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+                1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+                -0.5 0.866025 1.0 0.0 0.0 0.0 0.0 0.0
+                -0.5 -0.866025 -0.5 0.866025 1.0 0.0 0.0 0.0
+                1.0 -6.10623e-16 -0.5 -0.866025 -0.5 0.866025 1.0 0.0
+                -0.5 0.866025 1.0 -6.10623e-16 -0.5 -0.866025 -0.5 0.866025
+            ],
+            atol=1e-6,
+        ),
+    )
+
+    model2 = StateSpaceLearning.StructuralModel(
+        rand(4);
+        level=true,
+        stochastic_level=true,
+        trend=true,
+        stochastic_trend=true,
+        seasonal=true,
+        stochastic_seasonal=true,
+        freq_seasonal=2,
+        outlier=true,
+        cycle_period=3,
+        stochastic_cycle=true,
+        ζ_ω_threshold=0,
+        Exogenous_X=zeros(10, 0),
+        stochastic_start=3,
+    )
+    X5 = StateSpaceLearning.get_innovation_simulation_X(model2, innovation4, steps_ahead)
+    @assert all(
+        isapprox.(
+            X5,
+            [
+                0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+                0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+                1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+                -0.5 0.866025 1.0 0.0 0.0 0.0 0.0 0.0
+                -0.5 -0.866025 -0.5 0.866025 1.0 0.0 0.0 0.0
+                1.0 -6.10623e-16 -0.5 -0.866025 -0.5 0.866025 1.0 0.0
+                -0.5 0.866025 1.0 -6.10623e-16 -0.5 -0.866025 -0.5 0.866025
             ],
             atol=1e-6,
         ),