add unobserved_components_explanatory model #329
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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] |
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Just out of curiosity, what is this time series?
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Just random exogenous features. The different versions in Julia in tests were generating different numbers even with a fixed seed
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| # 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) | ||
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| # 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 | ||
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| # # 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) |
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| # "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 |
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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.
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I will implement this change and submit again
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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.