add unobserved_components_explanatory model #329
Closed
+244
−66
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
guilhermebodin
requested changes
Oct 8, 2024
| nile = CSV.File(StateSpaceModels.NILE) |> DataFrame | ||
| steps_ahead = 10 | ||
| nile_y = nile.flow | ||
| X = [0.906 0.247 0.065; 0.443 0.211 0.778; 0.745 0.32 0.081; 0.512 0.011 0.95; 0.253 0.99 0.136; 0.334 0.285 0.345; 0.427 0.078 0.449; 0.867 0.468 0.119; 0.099 0.476 0.04; 0.125 0.157 0.979; 0.692 0.328 0.558; 0.136 0.279 0.91; 0.032 0.022 0.346; 0.35 0.321 0.748; 0.93 0.852 0.115; 0.959 0.023 0.291; 0.774 0.631 0.059; 0.183 0.62 0.563; 0.297 0.434 0.124; 0.15 0.788 0.941; 0.893 0.851 0.298; 0.354 0.623 0.814; 0.131 0.355 0.719; 0.941 0.847 0.823; 0.057 0.719 0.176; 0.245 0.064 0.287; 0.805 0.366 0.027; 0.337 0.28 0.191; 0.823 0.893 0.4; 0.45 0.054 0.463; 0.891 0.661 0.365; 0.711 0.379 0.718; 0.36 0.792 0.0; 0.259 0.398 0.783; 0.39 0.832 0.179; 0.461 0.828 0.061; 0.934 0.575 0.829; 0.753 0.903 0.832; 0.729 0.28 0.71; 0.162 0.505 0.684; 0.637 0.266 0.309; 0.991 0.824 0.73; 0.383 0.371 0.828; 0.618 0.542 0.516; 0.484 0.503 0.101; 0.599 0.443 0.559; 0.453 0.087 0.414; 0.324 0.545 0.931; 0.51 0.596 0.049; 0.656 0.677 0.855; 0.869 0.206 0.392; 0.373 0.78 0.09; 0.692 0.564 0.374; 0.746 0.839 0.649; 0.096 0.342 0.578; 0.459 0.999 0.078; 0.608 0.776 0.99; 0.836 0.719 0.063; 0.283 0.652 0.737; 0.631 0.752 0.883; 0.691 0.081 0.348; 0.821 0.364 0.711; 0.564 0.881 0.045; 0.03 0.6 0.734; 0.091 0.975 0.586; 0.766 0.965 0.567; 0.246 0.14 0.612; 0.096 0.371 0.774; 0.092 0.797 0.055; 0.437 0.096 0.641; 0.231 0.895 0.384; 0.647 0.634 0.325; 0.681 0.612 0.507; 0.122 0.304 0.524; 0.648 0.364 0.023; 0.587 0.294 0.481; 0.377 0.61 0.275; 0.24 0.267 0.63; 0.222 0.24 0.283; 0.509 0.195 0.741; 0.218 0.944 0.804; 0.834 0.72 0.432; 0.563 0.016 0.229; 0.42 0.571 0.976; 0.62 0.31 0.238; 0.292 0.786 0.647; 0.145 0.938 0.018; 0.864 0.918 0.967; 0.926 0.09 0.712; 0.068 0.688 0.407; 0.047 0.739 0.862; 0.426 0.323 0.057; 0.972 0.092 0.697; 0.928 0.761 0.841; 0.077 0.757 0.522; 0.538 0.98 0.388; 0.357 0.843 0.108; 0.471 0.2 0.491; 0.97 0.026 0.486; 0.612 0.999 0.249; 0.539 0.877 0.886; 0.156 0.892 0.617; 0.159 0.57 0.768; 0.485 0.081 0.468; 0.924 0.506 0.68; 0.219 0.068 0.711; 0.457 0.461 0.514; 0.87 0.21 0.412; 0.907 0.305 0.581; 0.688 0.441 0.311] |
There was a problem hiding this comment.
Just out of curiosity, what is this time series?
There was a problem hiding this comment.
Just random exogenous features. The different versions in Julia in tests were generating different numbers even with a fixed seed
Comment on lines
51
to
79
| # rj_temp = CSV.File(RJ_TEMPERATURE) |> DataFrame | ||
| # rj_tem_values = rj_temp.Values ./ 100 | ||
| # steps_ahead = 52 | ||
| # X = rand(length(rj_tem_values) + steps_ahead, 1) | ||
| # β = [0.1] | ||
| # X_train = X[1:end - steps_ahead, :] | ||
| # X_test = X[end - steps_ahead + 1:end, :] | ||
| # rj_tem_values += X_train*β | ||
| # model = UnobservedComponentsExplanatory(rj_tem_values, X_train; trend = "local level", cycle = "stochastic") | ||
| # fit!(model) | ||
| # model.hyperparameters | ||
| # forec = forecast(model, X_test) | ||
|
|
||
| # TODO check with other software maybe statsmodels | ||
| # @test loglike(model) ≈ -1032.40953 atol = 1e-5 rtol = 1e-5 | ||
| # filt = kalman_filter(model) | ||
| # forec = forecast(model, X_test) | ||
| # @test monotone_forecast_variance(forec) | ||
| # smoother = kalman_smoother(model) | ||
| # alpha = get_smoothed_state(smoother) | ||
| # @test maximum(alpha[:, 1]) >= 296 | ||
| # @test minimum(alpha[:, 1]) <= 296 | ||
| # @test maximum(alpha[:, 2]) >= 7.5 | ||
| # @test minimum(alpha[:, 2]) <= -7.5 | ||
|
|
||
| # # Testing that it does not break | ||
| # rj_temp = CSV.File(RJ_TEMPERATURE) |> DataFrame | ||
| # model = UnobservedComponents(rj_temp.Values; trend = "smooth trend", cycle = "stochastic") | ||
| # fit!(model) |
Comment on lines
1
to
107
| # "linear deterministic trend", | ||
| # "linear trend", | ||
| "local linear trend", | ||
| # "damped local lineaar trend", | ||
| # "random walk with drift" | ||
| "smooth trend" | ||
| ] | ||
| return true | ||
| end | ||
| function parse_trend(trend::String) | ||
| validate_trend(trend) | ||
| (has_irregular, has_trend, stochastic_trend, | ||
| has_slope, stochastic_slope) = (false, false, false, false, false) | ||
| if trend == "local level" | ||
| (has_irregular, has_trend, stochastic_trend, | ||
| has_slope, stochastic_slope) = (true, true, true, false, false) | ||
| elseif trend == "random walk" | ||
| (has_irregular, has_trend, stochastic_trend, | ||
| has_slope, stochastic_slope) = (false, true, true, false, false) | ||
| elseif trend == "local linear trend" | ||
| (has_irregular, has_trend, stochastic_trend, | ||
| has_slope, stochastic_slope) = (true, true, true, true, true) | ||
| elseif trend == "smooth trend" | ||
| (has_irregular, has_trend, stochastic_trend, | ||
| has_slope, stochastic_slope) = (true, true, false, true, true) | ||
| end | ||
| # Validate trend booleans | ||
| if !has_trend && stochastic_trend | ||
| error("Invalid trend specification.") | ||
| end | ||
| if !has_trend && has_slope | ||
| error("Invalid trend specification.") | ||
| end | ||
| if !has_slope && stochastic_slope | ||
| error("Invalid trend specification.") | ||
| end | ||
| return (has_irregular, has_trend, stochastic_trend, has_slope, stochastic_slope) | ||
| end | ||
| function validate_seasonal(seasonal::String) | ||
| if seasonal == "no" | ||
| return true | ||
| else | ||
| spl = split(seasonal) | ||
| # TODO better error messaage | ||
| # Maybe a no string can be also valid | ||
| @assert length(spl) == 2 | ||
| @assert spl[1] in ["deterministic", "stochastic"] | ||
| return true | ||
| end | ||
| end | ||
| function parse_seasonal(seasonal::String) | ||
| validate_seasonal(seasonal) | ||
| if seasonal == "no" | ||
| has_seasonal, stochastic_seasonal, seasonal_freq = (false, false, 0) | ||
| return has_seasonal, stochastic_seasonal, seasonal_freq | ||
| else | ||
| spl = split(seasonal) | ||
| stochastic_seasonal = spl[1] == "stochastic" | ||
| seasonal_freq = parse(Int, spl[2]) | ||
| has_seasonal = seasonal_freq == 0 ? false : true | ||
| return has_seasonal, stochastic_seasonal, seasonal_freq | ||
| end | ||
| end | ||
| function validate_cycle(cycle::String) | ||
| if cycle == "no" | ||
| return true | ||
| else | ||
| spl = split(cycle) | ||
| # TODO better error messaage | ||
| if length(spl) == 1 | ||
| @assert spl[1] in ["deterministic", "stochastic"] | ||
| return true | ||
| elseif length(spl) == 2 | ||
| @assert spl[1] in ["deterministic", "stochastic"] | ||
| @assert spl[2] == "damped" | ||
| return true | ||
| end | ||
| end | ||
| return false | ||
| end | ||
| function parse_cycle(cycle::String) | ||
| validate_cycle(cycle) | ||
| if cycle == "no" | ||
| has_cycle, stochastic_cycle, damped_cycle = (false, false, false) | ||
| return has_cycle, stochastic_cycle, damped_cycle | ||
| else | ||
| spl = split(cycle) | ||
| has_cycle = true | ||
| # TODO better error messaage | ||
| # Maybe a no string can be also valid | ||
| if length(spl) == 1 | ||
| stochastic_cycle = spl[1] == "stochastic" | ||
| return has_cycle, stochastic_cycle, false | ||
| elseif length(spl) == 2 | ||
| stochastic_cycle = spl[1] == "stochastic" | ||
| damped_cycle = spl[2] == "damped" | ||
| return has_cycle, stochastic_cycle, damped_cycle | ||
| end | ||
| end | ||
| end |
There was a problem hiding this comment.
I feel like these are duplicates from Unobserved Components. Honestly, I think that the explanatory variables should be implemented in the Unobserved Components model. We could use the TimeVariant filter even if we don't have explanatory variables, I believe this is the best way of not having to maintain two nearly identical codebases.
There was a problem hiding this comment.
I will implement this change and submit again
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Add the unobserved components explanatory model. Unfortunately, including the cycle component results in instability, consistently leading to some hyperparameters being NaN. However, the other components perform well.