add multiple seasonality feature

.gitignore

@@ -12,4 +12,5 @@ paper_tests/mv_probabilistic_forecast/
 
 docs/build
 longhorizon/
-test.jl
+test.jl
+test_ms.jl

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "liveServer.settings.port": 5501
+}

Project.toml

@@ -11,7 +11,13 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
+[weakdeps]
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+
+[extensions]
+PlotsExt = "Plots"
+
 [compat]
-GLMNet = "0.5.0, 0.5.1, 0.5.2, 0.6.0, 0.6.1, 0.6.2, 0.6.3, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.7.4"
 Distributions = "0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
+GLMNet = "0.5.0, 0.5.1, 0.5.2, 0.6.0, 0.6.1, 0.6.2, 0.6.3, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.7.4"
 julia = "1"

README.md

@@ -56,6 +56,7 @@ Current features include:
 Quick example of fit and forecast for the air passengers time-series.
 
 ```julia
+using StateSpaceLearning
 using CSV
 using DataFrames
 using Plots
@@ -69,20 +70,15 @@ fit!(model)
 prediction_log = StateSpaceLearning.forecast(model, 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")
+plot_point_forecast(airp.passengers, prediction)
 ```
 ![quick_example_airp](./docs/src/assets/quick_example_airp.PNG)
 
 ```julia
 N_scenarios = 1000
 simulation = StateSpaceLearning.simulate(model, 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")
+plot_scenarios(airp.passengers, exp.(simulation))
 
 ```
 ![airp_sim](./docs/src/assets/airp_sim.svg)
@@ -103,7 +99,7 @@ fit!(model)
 
 level = model.output.components["μ1"]["Values"] + model.output.components["ξ"]["Values"]
 slope = model.output.components["ν1"]["Values"] + model.output.components["ζ"]["Values"]
-seasonal = model.output.components["γ1"]["Values"] + model.output.components["ω"]["Values"]
+seasonal = model.output.components["γ1_12"]["Values"] + model.output.components["ω_12"]["Values"]
 trend = level + slope
 
 plot(trend, w=2 , color = "Black", lab = "Trend Component", legend = :outerbottom)
@@ -216,7 +212,7 @@ ss_model = BasicStructural(log_air_passengers, 12)
 set_initial_hyperparameters!(ss_model, Dict("sigma2_ε" => residuals_variances["ε"], 
                                          "sigma2_ξ" =>residuals_variances["ξ"], 
                                          "sigma2_ζ" =>residuals_variances["ζ"], 
-                                         "sigma2_ω" =>residuals_variances["ω"]))
+                                         "sigma2_ω" =>residuals_variances["ω_12"]))
 StateSpaceModels.fit!(ss_model)
 ```