Model liniar cu inginerie de caracteristici: prețurile de argint cresc

Prețurile argintului au atins un nivel ridicat de 14 ani, pe fondul așteptărilor din ce în ce mai mari, că Rezerva Federală a SUA (Fed) va reduce ratele dobânzilor în această lună.

Potrivit modelului de învățare automată, benzile au scăzut, iar prețul este peste banda superioară, ceea ce indică niveluri anomale de preț.

Cod sursă:

library(tidyverse)
library(tidymodels)
library(tidyquant)
library(timetk)
library(modeltime)

#Silver Futures
df_silver <- 
  tq_get("SI=F") %>% 
  select(date, close) %>% 
  filter(date >= last(date) - months(36)) %>% 
  drop_na()


#Splitting the data
df_split <- 
  df_silver %>% 
  time_series_split(assess = "30 days", 
                    cumulative = TRUE)

df_train <- 
  training(df_split)

df_test <- 
  testing(df_split)

# Turn the normal mean function into a rolling mean with a 5 row .period
mean_roll_5 <- slidify(mean, .period = 5, .align = "right")

#Preprocessing
rec_spec <- 
  recipe(close ~ ., data = df_train) %>% 
  step_timeseries_signature(date) %>% 
  step_mutate(slid_close = mean_roll_5(close)) %>% 
  step_impute_bag(slid_close) %>% 
  step_fourier(date, period = 365, K = 5) %>%
  step_rm(date) %>%
  step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_numeric_predictors())


#Model Specification
mod_spec <- 
  linear_reg() %>% 
  set_engine("lm")


#Training
wflow_fit <- 
  workflow() %>% 
  add_recipe(rec_spec) %>% 
  add_model(mod_spec) %>% 
  fit(df_train)

#Modeltime
df_modeltime <- 
  modeltime_table(wflow_fit)

#Calibrate the model to the testing set
calibration_tbl <- 
  df_modeltime %>%
  modeltime_calibrate(new_data = df_test)


#Accuracy of the finalized model
calibration_tbl %>%
  modeltime_accuracy(metric_set = metric_set(rmse, rsq, mape))

  
#Prediction Intervals
calibration_tbl %>%
  modeltime_forecast(
    new_data    = df_test,
    actual_data = df_test
  ) %>% 
  plot_modeltime_forecast(
    .interactive = FALSE,
    .line_size = 1.5
  )  +
  labs(title = "Silver Futures", 
       subtitle = "Predictive Intervals of ML Model Model", 
       y = "", x = "") + 
  scale_y_continuous(labels = scales::label_currency()) +
  scale_x_date(labels = scales::label_date("%b %d"),
               date_breaks = "4 days") +
  theme_minimal(base_family = "Roboto Slab", base_size = 16) +
  theme(plot.subtitle = ggtext::element_markdown(face = "bold"),
        plot.title = element_text(face = "bold"),
        plot.background = element_rect(fill = "azure", color = "azure"),
        panel.background = element_rect(fill = "snow", color = "snow"),
        axis.text = element_text(face = "bold"),
        axis.text.x = element_text(angle = 45, 
                                   hjust = 1, 
                                   vjust = 1),
        legend.position = "none")