diff --git a/README.md b/README.md index df03ce1..c763ae4 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,10 @@ 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/assets/quick_example_airp.PNG) +```julia 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 @@ -81,7 +84,7 @@ end plot!(vcat(ones(length(log_air_passengers)).*NaN, exp.(simulation[:, N_scenarios])), lab = "Scenarios Paths", α = 0.1 , color = "red") ``` -![quick_example_airp](./docs/assets/quick_example_airp.PNG) +![airp_sim](./docs/assets/airp_sim.svg) ### Component Extraction Quick example on how to perform component extraction in time series utilizing StateSpaceLearning. diff --git a/docs/assets/airp_sim.svg b/docs/assets/airp_sim.svg new file mode 100644 index 0000000..b3088c1 --- /dev/null +++ b/docs/assets/airp_sim.svg @@ -0,0 +1,1042 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/src/manual.md b/docs/src/manual.md index df03ce1..c763ae4 100644 --- a/docs/src/manual.md +++ b/docs/src/manual.md @@ -70,7 +70,10 @@ 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/assets/quick_example_airp.PNG) +```julia 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 @@ -81,7 +84,7 @@ end plot!(vcat(ones(length(log_air_passengers)).*NaN, exp.(simulation[:, N_scenarios])), lab = "Scenarios Paths", α = 0.1 , color = "red") ``` -![quick_example_airp](./docs/assets/quick_example_airp.PNG) +![airp_sim](./docs/assets/airp_sim.svg) ### Component Extraction Quick example on how to perform component extraction in time series utilizing StateSpaceLearning.