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A few weeks ago, I introduced a Forecasting API that I deployed on Heroku. Under the hood, this API is built on top of ahead (and through Python packages rpy2 and Flask); an R package for univariate and multivariate time series forecasting. As of October 13th, 2021, 5 forecasting methods are implemented in ahead:

armagarchf: univariate time series forecasting method using simulation of an ARMA(1, 1) – GARCH(1, 1)

dynrmf: univariate time series forecasting method adapted from forecast::nnetar to support any
Statistical/Machine learning model (such as Ridge Regression, Random Forest, Support Vector Machines, etc.)

eatf: univariate time series forecasting method based on combinations of forecast::ets, forecast::auto.arima, and forecast::thetaf

ridge2f: multivariate time series forecasting method, based on quasi-randomized networks and presented in this paper

varf: multivariate time series forecasting method using Vector AutoRegressive model (VAR, mostly here for benchmarking purpose)

In this section, we illustrate dynrmf for univariate time series forecasting, using Random Forest and SVMs. Do not hesitate to type ?dynrmf,
?armagarchf or ?eatf in R console for more details and examples.

par(mfrow=c(2, 2))
# Plotting forecasts
# With a Random Forest regressor, an horizon of 20,
# and a 95% prediction interval
plot(dynrmf(fdeaths, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
# With a Support Vector Machine regressor, an horizon of 20,
# and a 95% prediction interval
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "linear"), predict_func = predict))
plot(dynrmf(Nile, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(Nile, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "linear"), predict_func = predict))

Multivariate time series

In this section, we illustrate ridge2f and varf forecasting for multivariate time series.
Do not hesitate to type ?ridge2f or ?varf in R console for more details on both functions.

# Forecast using ridge2
# With 2 time series lags, an horizon of 10,
# and a 95% prediction interval
fit_obj_ridge2 <- ahead::ridge2f(fpp::insurance, lags = 2,
h = 10, level = 95)
# Forecast using VAR
fit_obj_VAR <- ahead::varf(fpp::insurance, lags = 2,
h = 10, level = 95)
# Plotting forecasts
# fpp::insurance contains 2 time series, Quotes and TV.advert
par(mfrow=c(2, 2))
plot(fit_obj_ridge2, "Quotes")
plot(fit_obj_VAR, "Quotes")
plot(fit_obj_ridge2, "TV.advert")
plot(fit_obj_VAR, "TV.advert")

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