Forecasting the Economy

Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization

Keep in mind that there’s no hyperparameter tuning in these examples. Hyperparameter tuning must be used in practice. Looking for reticulate and rpy2 experts to discuss speedups for this R package (port from the stable Python version) installation and loading. There’s still room for improvement in this R port, especially in terms of data structure (a data structure that would handle time series not as matrices) choices.

%load_ext rpy2.ipython

The rpy2.ipython extension is already loaded. To reload it, use:
  %reload_ext rpy2.ipython

!pip install nnetsauce

%%R

# 0 - packages -----
utils::install.packages(c("reticulate",
                          "remotes",
                          "forecast",
                          "fpp2"))
remotes::install_github("Techtonique/nnetsauce_r") # slow here

library("reticulate")
library("nnetsauce")
library("fpp2")

%%R

# 1 - data -----
set.seed(123)
X <- fpp2::uschange
idx_train <- 1:floor(0.8*nrow(X))
X_train <- X[idx_train, ]
X_test <- X[-idx_train, ]

%%R

# 2 - model fitting ---
obj_MTS <- nnetsauce::MTS(sklearn$linear_model$BayesianRidge(),
                          lags = 1L) # use a Bayesian model for uncertainty quantification
obj_DeepMTS <- nnetsauce::DeepMTS(sklearn$linear_model$ElasticNet(),
                                  lags = 1L,
                                  replications=100L,
                                  kernel='gaussian') # use Kernel density for uncertainty quantification
obj_MTS$fit(X_train)
obj_DeepMTS$fit(X_train)

%%R

# 3 - model predictions ---
preds_MTS <- obj_MTS$predict(h = nrow(X_test),
                      level = 95,
                      return_std = TRUE)
preds_DeepMTS <- obj_DeepMTS$predict(h=nrow(X_test),
                        level = 95)

100%|██████████| 100/100 [00:00<00:00, 3510.91it/s]
100%|██████████| 100/100 [00:00<00:00, 5638.11it/s]

%%R

# 4 - Graph ---
par(mfrow=c(2, 4))
for (series_id in c(2, 3, 4, 5))
{
  plot(1:nrow(X_test), X_test[, series_id],
       main = paste0("MTS (Bayesian) -- \n", colnames(fpp2::uschange)[series_id]),
       type='l', ylim = c(min(preds_MTS$lower[, series_id]),
                          max(preds_MTS$upper[, series_id])))
  lines(preds_MTS$lower[, series_id], col="blue", lwd=2)
  lines(preds_MTS$upper[, series_id], col="blue", lwd=2)
  lines(preds_MTS$mean[, series_id], col="red", lwd=2)
}
for (series_id in c(2, 3, 4, 5))
{
  plot(1:nrow(X_test), X_test[, series_id],
       main = paste0("DeepMTS (KDE) -- \n", colnames(fpp2::uschange)[series_id]),
       type='l', ylim = c(min(preds_DeepMTS$lower[, series_id]),
                          max(preds_DeepMTS$upper[, series_id])))
  lines(preds_DeepMTS$lower[, series_id], col="blue", lwd=2)
  lines(preds_DeepMTS$upper[, series_id], col="blue", lwd=2)
  lines(preds_DeepMTS$mean[, series_id], col="red", lwd=2)
}

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In this figure, KDE stands for Kernel Density Estimation. Prediction intervals are depicted as a blue line, and mean forecast as a red line. The true value is depicted as a black line. Again, keep in mind that every model is used with its default hyperparameters, and hyperparameters’ tuning will give a different result.

Visualizing predictive simulations for DeepMTS

%%R
par(mfrow=c(2, 2))
matplot(preds_DeepMTS$sims[[1]], type='l', col=1:4, lwd=2, lty=1, ylim=c(-40, 40))
matplot(preds_DeepMTS$sims[[25]], type='l', col=1:4, lwd=2, lty=1, ylim=c(-40, 40))
matplot(preds_DeepMTS$sims[[50]], type='l', col=1:4, lwd=2, lty=1, ylim=c(-40, 40))
matplot(preds_DeepMTS$sims[[100]], type='l', col=1:4, lwd=2, lty=1, ylim=c(-40, 40))

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