update glmnet

.github/workflows/documentation.yml

@@ -12,8 +12,8 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@latest
         with:
-          # Build documentation on Julia 1.0
-          version: '1.0'
+          # Build documentation on Julia 1.10
+          version: '1.10'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy

.gitignore

@@ -6,3 +6,5 @@ paper_tests/m4_test/metrics_results/
 paper_tests/m4_test/results_ARIMA/
 paper_tests/m4_test/results_SSL/
 paper_tests/simulation_test/results_simulation_raw/
+
+docs/build

Project.toml

@@ -1,7 +1,7 @@
 name = "StateSpaceLearning"
 uuid = "971c4b7c-2c4e-4bac-8525-e842df3cde7b"
 authors = ["andreramosfc <[email protected]>"]
-version = "1.0.1"
+version = "1.0.2"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -12,6 +12,6 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-GLMNet = "0.4.0, 0.4.1, 0.4.2, 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"
+GLMNet = "0.7.4"
 Distributions = "0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
-julia = "1"
+julia = "1.10"

docs/Project.toml

@@ -4,3 +4,8 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GLMNet = "8d5ece8b-de18-5317-b113-243142960cc6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+
+[compat]
+julia = "1.10"
+GLMNet = "0.7.4"
+Distributions = "0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"

docs/src/manual.md

@@ -68,7 +68,7 @@ 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")
 ```
-![quick_example_airp](./docs/src/assets/quick_example_airp.PNG)
+![quick_example_airp](assets/quick_example_airp.PNG)
 
 ```julia
 N_scenarios = 1000
@@ -81,7 +81,7 @@ end
 plot!(vcat(ones(length(log_air_passengers)).*NaN, exp.(simulation[:, N_scenarios])), lab = "Scenarios Paths", α = 0.1 , color = "red")
 
 ```
-![airp_sim](./docs/src/assets/airp_sim.svg)
+![airp_sim](assets/airp_sim.svg)
 
 ### Component Extraction
 Quick example on how to perform component extraction in time series utilizing StateSpaceLearning.
@@ -107,7 +107,7 @@ plot(seasonal, w=2 , color = "Black", lab = "Seasonal Component", legend = :oute
 
 ```
 
-| ![quick_example_trend](./docs/src/assets/trend.svg) | ![quick_example_seas](./docs/src/assets/seasonal.svg)|
+| ![quick_example_trend](assets/trend.svg) | ![quick_example_seas](assets/seasonal.svg)|
 |:------------------------------:|:-----------------------------:|
 
 
@@ -164,7 +164,7 @@ 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")
 
 ```
-![quick_example_completion_airp](./docs/src/assets/quick_example_completion_airp.PNG)
+![quick_example_completion_airp](assets/quick_example_completion_airp.PNG)
 
 ### Outlier Detection
 Quick example of outlier detection for an altered air passengers time-series (artificial NaN values are added to the original time-series).
@@ -190,7 +190,7 @@ plot(log_air_passengers, w=2 , color = "Black", lab = "Historical", legend = :ou
 scatter!([detected_outliers], log_air_passengers[detected_outliers], lab = "Detected Outliers")
 
 ```
-![quick_example_completion_airp](./docs/src/assets/outlier.svg)
+![quick_example_completion_airp](assets/outlier.svg)
 
 ### StateSpaceModels initialization
 Quick example on how to use StateSpaceLearning to initialize  StateSpaceModels