📝add outer docs

README.md

@@ -84,7 +84,6 @@ using StateSpaceModels
 airp = CSV.File(StateSpaceModels.AIR_PASSENGERS) |> DataFrame
 airpassengers = (airp.passengers).*1.0
 log_air_passengers = log.(airpassengers)
-steps_ahead = 30
 
 log_air_passengers[60:72] .= NaN
 

docs/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"

docs/make.jl

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+using Documenter
+using StateSpaceLearning
+
+# Set up to run docstrings with jldoctest
+DocMeta.setdocmeta!(
+    StateSpaceLearning, :DocTestSetup, :(using StateSpaceLearning); recursive=true
+)
+
+makedocs(;
+    modules=[StateSpaceLearning],
+    doctest=true,
+    clean=true,
+    format=Documenter.HTML(mathengine=Documenter.MathJax2()),
+    sitename="StateSpaceLearning.jl",
+    authors="André Ramos",
+    pages=[
+        "Home" => "index.md",
+        "manual.md"
+    ],
+)
+
+deploydocs(
+        repo="github.com/LAMPSPUC/StateSpaceLearning.jl.git",
+        push_preview = true
+    )

docs/src/index.md

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+```@raw html
+<div style="width:100%; height:150px;border-width:4px;border-style:solid;padding-top:25px;
+        border-color:#000;border-radius:10px;text-align:center;background-color:#99DDFF;
+        color:#000">
+    <h3 style="color: black;">Star us on GitHub!</h3>
+    <a class="github-button" href="https://github.com/LAMPSPUC/StateSpaceLearning.jl" data-icon="octicon-star" data-size="large" data-show-count="true" aria-label="Star LAMPSPUC/StateSpaceLearning.jl on GitHub" style="margin:auto">Star</a>
+    <script async defer src="https://buttons.github.io/buttons.js"></script>
+</div>
+```
+
+# StateSpaceLearning.jl Documentation
+
+StateSpaceLearning.jl is a package for modeling and forecasting time series in a high-dimension regression framework.
+
+## Installation
+
+This package is registered so you can simply `add` it using Julia's `Pkg` manager:
+```julia
+pkg> add StateSpaceLearning
+```

docs/src/manual.md

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+# Manual
+
+## Quick Start Guide
+
+Although StateSpaceLearning.jl has a lot of functionalities, different models and interfaces 
+users usuallly just want to fit a model and analyse the residuals, components and make some forecasts.
+The following code is a quick start to perform these tasks
+
+```julia
+using StateSpaceLearning
+using Plots
+
+y = randn(100)
+
+output = StateSpaceLearning.fit_model(y)
+
+prediction = StateSpaceLearning.forecast(output, 12)
+
+plot(y, w=2 , color = "Black", lab = "Historical", legend = :outerbottom)
+plot!(output.fitted, w=2 , color = "Black", lab = "Fit In Sample", legend = :outerbottom)
+plot!(vcat(ones(output.T).*NaN, prediction), lab = "Forcast", w=2, color = "blue")
+
+```
+### Completion of missing values
+Quick example of completion of missing values for the air passengers time-series (artificial NaN values are added to the original time-series).
+
+```julia
+
+y = rand(144)
+
+y[60:72] .= NaN
+
+output = StateSpaceLearning.fit_model(y)
+
+completed_values = output.fitted[60:72]
+
+```
+
+### Component extraction
+Quick example of component extraction in a time series
+
+```julia
+using CSV
+using DataFrames
+using Plots
+using StateSpaceModels
+
+airp = CSV.File(StateSpaceModels.AIR_PASSENGERS) |> DataFrame
+log_air_passengers = log.(airp.passengers)
+
+output = StateSpaceLearning.fit_model(log_air_passengers)
+
+μ₁ = output.components["μ₁"]["Values"]
+ν₁ = output.components["ν₁"]["Values"]
+γ₁ = output.components["γ₁"]["Values"]
+ξ  = output.components["ξ"]["Values"]
+ζ  = output.components["ζ"]["Values"]
+ω  = output.components["ω"]["Values"]
+
+```
+
+### Models
+
+The package provides a variaty of pre-defined models. If there is any model that you wish was in the package, feel free to open an issue or pull request.
+
+### Basic Structural Model
+The basic structural state-space model consists of a trend (level + slope) and a seasonal
+component. It is defined by:
+
+```math
+\begin{gather*}
+    \begin{aligned}
+        y_{t} &=  \mu_{t} + \gamma_{t} + \varepsilon_{t} \quad &\varepsilon_{t} \sim \mathcal{N}(0, \sigma^2_{\varepsilon})\\
+        \mu_{t+1} &= \mu_{t} + \nu_{t} + \xi_{t} \quad &\xi_{t} \sim \mathcal{N}(0, \sigma^2_{\xi})\\
+        \nu_{t+1} &= \nu_{t} + \zeta_{t} \quad &\zeta_{t} \sim \mathcal{N}(0, \sigma^2_{\zeta})\\
+        \gamma_{t+1} &= -\sum_{j=1}^{s-1} \gamma_{t+1-j} + \omega_{t} \quad & \omega_{t} \sim \mathcal{N}(0, \sigma^2_{\omega})\\
+    \end{aligned}
+\end{gather*}
+```
+
+#### References
+ * Durbin, James, & Siem Jan Koopman. (2012). "Time Series Analysis by State Space Methods: Second Edition." Oxford University Press.
+
+
+### Local Level Model
+
+The local level model is defined by:
+```math
+\begin{gather*}
+    \begin{aligned}
+        y_{t} &=  \mu_{t} + \varepsilon_{t} \quad \varepsilon_{t} \sim \mathcal{N}(0, \sigma^2_{\varepsilon})\\
+        \mu_{t+1} &= \mu_{t} + \eta_{t} \quad \eta_{t} \sim \mathcal{N}(0, \sigma^2_{\eta})\\
+    \end{aligned}
+\end{gather*}
+```
+#### References
+ * Durbin, James, & Siem Jan Koopman. (2012). "Time Series Analysis by State Space Methods: Second Edition." Oxford University Press. pp. 9
+
+### Local Linear Trend 
+The linear trend model is defined by:
+```math
+\begin{gather*}
+    \begin{aligned}
+        y_{t} &=  \mu_{t} + \gamma_{t} + \varepsilon_{t} \quad &\varepsilon_{t} \sim \mathcal{N}(0, \sigma^2_{\varepsilon})\\
+        \mu_{t+1} &= \mu_{t} + \nu_{t} + \xi_{t} \quad &\xi_{t} \sim \mathcal{N}(0, \sigma^2_{\xi})\\
+        \nu_{t+1} &= \nu_{t} + \zeta_{t} \quad &\zeta_{t} \sim \mathcal{N}(0, \sigma^2_{\zeta})\\
+    \end{aligned}
+\end{gather*}
+```
+#### References
+ * Durbin, James, & Siem Jan Koopman. (2012). "Time Series Analysis by State Space Methods:
+    Second Edition." Oxford University Press. pp. 44
+
+### Implementing a custom model
+
+Users are able to implement any custom user-defined model. For that, it is necessary to modify "model_utils.jl" file. More specificly, it is necessary to modify "create_X" function to be able to create the corresponding high dimension regression matrix and "get_components_indexes" function to assess the correct compenents in the new proposed model.
+