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fix simulation for model with level and without slope #79

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Merged
merged 1 commit into from
Aug 13, 2025
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fix simulation for model with level and without slope #79

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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "StateSpaceLearning"
uuid = "971c4b7c-2c4e-4bac-8525-e842df3cde7b"
authors = ["andreramosfc <[email protected]>"]
version = "2.0.9"
version = "2.0.10"

[deps]
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
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10 changes: 5 additions & 5 deletions src/models/structural_model.jl
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Expand Up @@ -95,10 +95,10 @@ mutable struct StructuralModel <: StateSpaceLearningModel

@assert !has_intercept(exog) "Exogenous matrix must not have an intercept column"
@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 level in ["deterministic", "stochastic", "none"] "level must be either deterministic, stochastic or no"
@assert slope in ["deterministic", "stochastic", "none"] "slope must be either deterministic, stochastic or no"
@assert seasonal in ["deterministic", "stochastic", "none"] "seasonal must be either deterministic, stochastic or no"
@assert cycle in ["deterministic", "stochastic", "none"] "cycle must be either deterministic, stochastic or no"
@assert level in ["deterministic", "stochastic", "none"] "level must be either deterministic, stochastic or none"
@assert slope in ["deterministic", "stochastic", "none"] "slope must be either deterministic, stochastic or none"
@assert seasonal in ["deterministic", "stochastic", "none"] "seasonal must be either deterministic, stochastic or none"
@assert cycle in ["deterministic", "stochastic", "none"] "cycle must be either deterministic, stochastic or none"
@assert seasonal != "none" ? length(y) > minimum(freq_seasonal) : true "Time series must be longer than the seasonal period if seasonal is added"

if typeof(freq_seasonal) <: Vector
Expand Down Expand Up @@ -1591,7 +1591,7 @@ function simulate_states(
)
end

model.slope ? slope = ones(T, N_scenarios) .* slope : nothing
slope = ones(T, N_scenarios) .* slope
model.level ? trend = ones(T, N_scenarios) .* trend : nothing
model.seasonal ? seasonals = [ones(T, N_scenarios) .* s for s in seasonals] : nothing
for t in (T + 1):(T + steps_ahead)
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