Conformal Prediction Intervals of XGBoost Model: Bitcoin Peaks Amid Coinbase’s Earnings Struggle

In the second quarter, Coinbase did not meet Wall Street’s expectations. This decline occurred alongside lower market volatility, despite BTC prices reaching record highs according to Kaiko Research.

The chart below indicates that the market priced the aforementioned from August. We can observe a negative decoupling between the Bitcoin and Coinbase Global share prices.

Source code:

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


#Coinbase Global
df_coin <- 
  tq_get("COIN") %>% 
  select(date, Coinbase = close)

#Bitcoin
df_btc <- 
  tq_get("BTC-USD") %>% 
  select(date, Bitcoin = close)

#Merging the datasets
df_merged <- 
  df_coin %>% 
  left_join(df_btc) %>% 
  drop_na() %>% 
  filter(date >= last(date) - months(36)) %>% 
  pivot_longer(-date,
               names_to = "id",
               values_to = "close") %>% 
  mutate(id = as_factor(id)) 


#Split Data
splits <- 
  time_series_split(
  df_merged,
  assess     = "15 days",
  cumulative = TRUE
)

#Create & Fit Forecasting Models

#Recipe
recipe_ml <- 
  recipe(close ~ ., training(splits)) %>%
  step_timeseries_signature(date) %>%
  step_rm(date) %>%
  step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_numeric_predictors())


#Model & Workflow
model_xgb <- 
  boost_tree("regression") %>%
  set_engine("xgboost")


wflw_fit_xgb <- 
  workflow() %>%
  add_model(model_xgb) %>%
  add_recipe(recipe_ml) %>%
  fit(training(splits))


#Adding fitted models to a Model Table
models_tbl <- modeltime_table(
  wflw_fit_xgb
)


#Calibrating the model to a testing set
calibration_tbl <- 
  models_tbl %>%
  modeltime_calibrate(
    new_data = testing(splits), 
    id       = "id"
  )


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


#Conformal Split Method
#https://business-science.github.io/modeltime/articles/modeltime-conformal-prediction.html

forecast_tbl <- 
  calibration_tbl %>%
  modeltime_forecast(
    new_data      = testing(splits),
    actual_data   = df_merged %>% filter(date >= as.Date("2025-07-23")),
    conf_interval = 0.95,
    conf_method   = "conformal_split", # Split Conformal Method
    conf_by_id    = TRUE, # TRUE = local CI by ID, FALSE = global CI
    keep_data     = TRUE
  )

#Plotting prediction intervals
forecast_tbl %>%
  group_by(id) %>%
  plot_modeltime_forecast(
    .facet_ncol  = 1, 
    .line_size = 1.5,
    .interactive = FALSE
  ) +
  labs(title = "<span style = 'color:dimgrey;'>Conformal Prediction Intervals</span> of <span style = 'color:red;'>XGBoost</span> Model", 
       y = "", 
       x = "") + 
  scale_y_continuous(labels = scales::label_currency()) +
  scale_x_date(labels = scales::label_date("%b %d"),
               date_breaks = "4 days") +
  theme_tq(base_family = "Roboto Slab", base_size = 16) +
  theme(plot.title = ggtext::element_markdown(face = "bold", 
                                              hjust = 0.5,
                                              size = 18),
        strip.text = element_text(face = "bold"),
        legend.position = "none")

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