StateSpaceLearning

using StateSpaceLearning

y = randn(100)

#Fit Model
output = StateSpaceLearning.fit_model(y)

#Main output options 
model_input         = output.model_input # Model inputs that were utilized to build the regression matrix.
Create_X            = output.Create_X # The function utilized to build the regression matrix.
X                   = output.X # High Dimension Regression utilized in the estimation.
coefs               = output.coefs # High Dimension Regression coefficients estimated in the estimation.
ε                   = output.ε # Residuals of the model.
fitted              = output.fitted # Fit in Sample of the model.
components          = output.components # Dictionary containing information about each component of the model, each component has the keys: "Values" (The value of the component in each timestamp) , "Coefs" (The coefficients estimated for each element of the component) and "Indexes" (The indexes of the elements of the component in the high dimension regression "X").
residuals_variances = output.residuals_variances # Dictionary containing the estimated variances for the innovations components (that is the information that can be utilized to initialize the state space model).
valid_indexes       = output.valid_indexes # Vector containing valid indexes of the time series (non valid indexes represent NaN values in the time series).

#Forecast
prediction = StateSpaceLearning.forecast(output, 12) #Gets a 12 steps ahead prediction

using CSV
using DataFrames
using Plots

airp = CSV.File(StateSpaceLearning.AIR_PASSENGERS) |> DataFrame
log_air_passengers = log.(airp.passengers)
steps_ahead = 30

output = StateSpaceLearning.fit_model(log_air_passengers)
prediction_log = StateSpaceLearning.forecast(output, steps_ahead) # arguments are the output of the fitted model and number of steps ahead the user wants to forecast
prediction = exp.(prediction_log)

plot(airp.passengers, w=2 , color = "Black", lab = "Historical", legend = :outerbottom)
plot!(vcat(ones(length(log_air_passengers)).*NaN, prediction), lab = "Forecast", w=2, color = "blue")

N_scenarios = 1000
simulation = StateSpaceLearning.simulate(output, steps_ahead, N_scenarios) # arguments are the output of the fitted model, number of steps ahead the user wants to forecast and number of scenario paths

plot(airp.passengers, w=2 , color = "Black", lab = "Historical", legend = :outerbottom)
for s in 1:N_scenarios-1
    plot!(vcat(ones(length(log_air_passengers)).*NaN, exp.(simulation[:, s])), lab = "", α = 0.1 , color = "red")
end
plot!(vcat(ones(length(log_air_passengers)).*NaN, exp.(simulation[:, N_scenarios])), lab = "Scenarios Paths", α = 0.1 , color = "red")

using CSV
using DataFrames
using Plots

airp = CSV.File(StateSpaceLearning.AIR_PASSENGERS) |> DataFrame
log_air_passengers = log.(airp.passengers)

output = StateSpaceLearning.fit_model(log_air_passengers)

level = output.components["μ1"]["Values"] + output.components["ξ"]["Values"]
slope = output.components["ν1"]["Values"] + output.components["ζ"]["Values"]
seasonal = output.components["γ1"]["Values"] + output.components["ω"]["Values"]
trend = level + slope

plot(trend, w=2 , color = "Black", lab = "Trend Component", legend = :outerbottom)
plot(seasonal, w=2 , color = "Black", lab = "Seasonal Component", legend = :outerbottom)

using StateSpaceLearning
using CSV
using DataFrames
using Random

Random.seed!(2024)

airp = CSV.File(StateSpaceLearning.AIR_PASSENGERS) |> DataFrame
log_air_passengers = log.(airp.passengers)
X = rand(length(log_air_passengers), 10) # Create 10 exogenous features 
β = rand(3)

y = log_air_passengers + X[:, 1:3]*β # add to the log_air_passengers series a contribution from only 3 exogenous features.

output = StateSpaceLearning.fit_model(y; Exogenous_X = X, estimation_input = Dict("α" => 1.0, "information_criteria" => "bic", "ε" => 0.05, "penalize_exogenous" => true, "penalize_initial_states" => true))

Selected_exogenous = output.components["Exogenous_X"]["Selected"]

using CSV
using DataFrames
using Plots

airp = CSV.File(StateSpaceLearning.AIR_PASSENGERS) |> DataFrame
log_air_passengers = log.(airp.passengers)

airpassengers = Float64.(airp.passengers)
log_air_passengers[60:72] .= NaN

output = StateSpaceLearning.fit_model(log_air_passengers)

fitted_completed_missing_values = ones(144).*NaN; fitted_completed_missing_values[60:72] = exp.(output.fitted[60:72])
real_removed_valued = ones(144).*NaN; real_removed_valued[60:72] = deepcopy(airp.passengers[60:72])
airpassengers[60:72] .= NaN

plot(airpassengers, w=2 , color = "Black", lab = "Historical", legend = :outerbottom)
plot!(real_removed_valued, lab = "Real Removed Values", w=2, color = "red")
plot!(fitted_completed_missing_values, lab = "Fit in Sample completed values", w=2, color = "blue")

using CSV
using DataFrames
using Plots

airp = CSV.File(StateSpaceLearning.AIR_PASSENGERS) |> DataFrame
log_air_passengers = log.(airp.passengers)

log_air_passengers[60] = 10
log_air_passengers[30] = 1
log_air_passengers[100] = 2

output = StateSpaceLearning.fit_model(log_air_passengers)
detected_outliers = findall(i -> i != 0, output.components["o"]["Coefs"])

plot(log_air_passengers, w=2 , color = "Black", lab = "Historical", legend = :outerbottom)
scatter!([detected_outliers], log_air_passengers[detected_outliers], lab = "Detected Outliers")

using CSV
using DataFrames
using StateSpaceModels

airp = CSV.File(StateSpaceLearning.AIR_PASSENGERS) |> DataFrame
log_air_passengers = log.(airp.passengers)

output = StateSpaceLearning.fit_model(log_air_passengers)
residuals_variances = output.residuals_variances

model = BasicStructural(log_air_passengers, 12)
set_initial_hyperparameters!(model, Dict("sigma2_ε" => residuals_variances["ε"], 
                                         "sigma2_ξ" =>residuals_variances["ξ"], 
                                         "sigma2_ζ" =>residuals_variances["ζ"], 
                                         "sigma2_ω" =>residuals_variances["ω"]))
fit!(model)