add cycle component

.gitignore

@@ -13,4 +13,6 @@ paper_tests/mv_probabilistic_forecast/
 docs/build
 longhorizon/
 test.jl
-test_ms.jl
+test_ms.jl
+new/
+test2.jl

Project.toml

@@ -1,13 +1,14 @@
 name = "StateSpaceLearning"
 uuid = "971c4b7c-2c4e-4bac-8525-e842df3cde7b"
 authors = ["andreramosfc <[email protected]>"]
-version = "1.3.2"
+version = "1.4.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GLMNet = "8d5ece8b-de18-5317-b113-243142960cc6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
@@ -20,4 +21,5 @@ PlotsExt = "Plots"
 [compat]
 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"
+SparseArrays = "^1.0.0"
 julia = "1"

paper_tests/m4_test/m4_test.jl

@@ -25,6 +25,8 @@ function append_results(filepath, results_df)
     if isfile(filepath)
         df_old = CSV.read(filepath, DataFrame)
         results_df = vcat(df_old, results_df)
+        @info "MASE avg = $(mean(results_df[:, :MASE]))"
+        @info "sMAPE avg = $(mean(results_df[:, :sMAPE]))"
     end
     return CSV.write(filepath, results_df)
 end
@@ -139,5 +141,3 @@ end
 create_dirs()
 
 main()
-
-#run_config(DataFrame(), false, "aic", 0.1, true, 2794)#max sample size

paper_tests/m4_test/m4_test.py

@@ -3,7 +3,12 @@
 import statsmodels.api as sm
 import numpy as np
 
-df_train = pd.read_csv("paper_tests/m4_test/Monthly-train.csv")
+df_train1 = pd.read_csv("paper_tests/m4_test/Monthly-train1.csv")
+df_train2 = pd.read_csv("paper_tests/m4_test/Monthly-train2.csv")
+df_train3 = pd.read_csv("paper_tests/m4_test/Monthly-train3.csv")
+df_train4 = pd.read_csv("paper_tests/m4_test/Monthly-train4.csv")
+df_train = pd.concat([df_train1, df_train2, df_train3, df_train4])
+m4_info = pd.read_csv("paper_tests/m4_test/M4-info.csv")
 df_test = pd.read_csv("paper_tests/m4_test/Monthly-test.csv")
 ssl_init_df = pd.read_csv("paper_tests/m4_test/init_SSL/SSL_aic_0.1_false.csv")
 

src/StateSpaceLearning.jl

@@ -1,6 +1,6 @@
 module StateSpaceLearning
 
-using LinearAlgebra, Statistics, GLMNet, Distributions
+using LinearAlgebra, Statistics, GLMNet, Distributions, SparseArrays
 
 abstract type StateSpaceLearningModel end
 

src/fit_forecast.jl

@@ -121,20 +121,7 @@ function forecast(
     @assert size(Exogenous_Forecast, 1) == steps_ahead "Exogenous_Forecast must have the same number of rows as steps_ahead"
 
     Exogenous_X = model.X[:, exog_idx]
-    complete_matrix = create_X(
-        model.level,
-        model.stochastic_level,
-        model.trend,
-        model.stochastic_trend,
-        model.seasonal,
-        model.stochastic_seasonal,
-        model.freq_seasonal,
-        model.outlier,
-        model.ζ_ω_threshold,
-        Exogenous_X,
-        steps_ahead,
-        Exogenous_Forecast,
-    )
+    complete_matrix = create_X(model, Exogenous_X, steps_ahead, Exogenous_Forecast)
 
     if typeof(model.output) == Output
         return AbstractFloat.(