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improve squared exponential trend extension
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,42 +1,100 @@ | ||
| # Generate series with independent latent GP trends | ||
| library(mvgam) | ||
| sim_data <- sim_mvgam(T = 70, n_series = 2, | ||
| sim_data <- sim_mvgam(T = 90, n_series = 2, | ||
| use_lv = F, | ||
| trend_model = 'GP', | ||
| trend_rel = 0.6, | ||
| family = 'poisson', | ||
| mu_obs = c(7, 10)) | ||
| mod1stan <- mvgam(data_train = sim_data$data_train, | ||
| data_test = sim_data$data_test, | ||
| formula = y ~ s(season, k = 12, bs = 'cc'), | ||
| knots = list(season = c(0.5, 12.5)), | ||
| family = 'poisson', | ||
| trend_model = 'GP', | ||
| chains = 4, | ||
| burnin = 1000, | ||
| use_stan = T, run_model = T) | ||
| mod1stan$model_file | ||
| summary(mod1stan) | ||
| plot(mod1stan, 'forecast', series = 2) | ||
| mu_obs = c(7, 10), | ||
| train_prop = 1) | ||
|
|
||
| # To run outside of mvgam (maybe show how other elements could be added?) | ||
| train_dat <- sim_data$data_train %>% | ||
| dplyr::filter(time <= 75) | ||
| test_dat <- sim_data$data_train %>% | ||
| dplyr::filter(time > 75) | ||
| # mod1stan <- mvgam(data_train = train_dat, | ||
| # data_test = test_dat, | ||
| # formula = y ~ s(season, k = 12, bs = 'cc'), | ||
| # knots = list(season = c(0.5, 12.5)), | ||
| # family = 'nb', | ||
| # trend_model = 'GP', | ||
| # use_stan = T, | ||
| # run_model = F) | ||
| # test <- rstan::stan(model_code = mod1stan$model_file, | ||
| # data = mod1stan$model_data, | ||
| # chains = 1, iter = 1000, | ||
| # init = mod1stan$inits) | ||
|
|
||
| mod1jags <- mvgam(data_train = train_dat, | ||
| data_test = test_dat, | ||
| formula = y ~ s(season, k = 12, bs = 'cc'), | ||
| knots = list(season = c(0.5, 12.5)), | ||
| family = 'poisson', | ||
| trend_model = 'AR3') | ||
|
|
||
| mod1stan <- mvgam(data_train = train_dat, | ||
| data_test = test_dat, | ||
| formula = y ~ s(season, k = 12, bs = 'cc'), | ||
| knots = list(season = c(0.5, 12.5)), | ||
| family = 'poisson', | ||
| trend_model = 'GP', | ||
| use_stan = T, | ||
| burnin = 1000) | ||
|
|
||
| # Compare the out of sample DRPS for each model | ||
| plot(mod1jags, 'forecast', series = 1, data_test = test_dat) | ||
| plot(mod1stan, 'forecast', series = 1, data_test = test_dat) | ||
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| plot(mod1jags, 'forecast', series = 2, data_test = test_dat) | ||
| plot(mod1stan, 'forecast', series = 2, data_test = test_dat) | ||
|
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| compare_mvgams(mod1jags, mod1stan, fc_horizon = 10, | ||
| n_evaluations = 20, n_cores = 4) | ||
|
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| plot(mod1stan, 'residuals', series = 1) | ||
| plot(mod1stan, 'residuals', series = 2) | ||
| plot(mod1stan, 'uncertainty', data_test = test_dat, series = 1) | ||
| plot(mod1stan, 'uncertainty', data_test = test_dat, series = 2) | ||
| plot(mod1stan, 'forecast', series = 1, data_test = test_dat) | ||
| plot(mod1stan, 'forecast', series = 2, data_test = test_dat) | ||
| plot(mod1stan, 'smooths', series = 1) | ||
| plot(mod1stan, 'smooths', series = 2) | ||
| plot(mod1stan, 'trend') | ||
| plot(mod1stan, 'trend', series = 2) | ||
| plot(sim_data$true_trends[,2]) | ||
|
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||
| pfilter_mvgam_init(object = mod1stan, data_assim = sim_data$data_test %>% | ||
| dplyr::filter(time == 54), | ||
| n_particles = 10000) | ||
| # Need to fix the particle filter so it can initiate even if only one series has a next observation | ||
| pfilter_mvgam_init(object = mod1stan, data_assim = test_dat, | ||
| n_particles = 1000) | ||
|
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||
| pfilter_mvgam_online(data_assim = sim_data$data_test[1:10, ], n_cores = 3, kernel_lambda = 1) | ||
| pfilter_mvgam_online(data_assim = test_dat[1:14, ], | ||
| n_cores = 3, use_resampling = T, | ||
| threshold = 0.6) | ||
|
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||
| fcs <- pfilter_mvgam_fc(data_test = sim_data$data_test, ylim = c(0, 17)) | ||
| fcs <- pfilter_mvgam_fc(data_test = test_dat, ylim = c(0, 20), n_cores = 3) | ||
|
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||
| layout(matrix(c(1:2), ncol = 2, nrow = 1)) | ||
| plot_mvgam_fc(mod1stan, series = 1, data_test = sim_data$data_test, | ||
| ylim = c(0, 17)) | ||
| layout(matrix(c(1:2), ncol = 1, nrow = 2)) | ||
| plot_mvgam_fc(mod1stan, series = 1, data_test = test_dat, | ||
| ylim = c(0, 20)) | ||
| fcs$series_1() | ||
| points(c(sim_data$data_train %>% | ||
| dplyr::filter(series == 'series_1')%>% | ||
| dplyr::pull(y), | ||
| sim_data$data_test %>% | ||
| dplyr::filter(series == 'series_1') %>% | ||
| dplyr::pull(y))) | ||
| dplyr::pull(y)), | ||
| pch = 16, cex = 0.4) | ||
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| layout(matrix(c(1:2), ncol = 1, nrow = 2)) | ||
| plot_mvgam_fc(mod1stan, series = 2, data_test = test_dat, | ||
| ylim = c(0, 20)) | ||
| fcs$series_2() | ||
| points(c(sim_data$data_train %>% | ||
| dplyr::filter(series == 'series_2')%>% | ||
| dplyr::pull(y), | ||
| sim_data$data_test %>% | ||
| dplyr::filter(series == 'series_2') %>% | ||
| dplyr::pull(y)), | ||
| pch = 16, cex = 0.4) | ||
| unlink('pfilter', recursive = T) |
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