Time Series Machine Learning: Shanghai Composite

Shanghai Composite does not seem to be at an ideal point for entry.

Source code:

library(tidyverse)library(tidyquant)library(timetk)library(tidymodels)library(modeltime)library(workflowsets)#Shanghai Composite Index (000001.SS)df_shanghai <-   tq_get("000001.SS", from = "2015-09-01") %>%   tq_transmute(select = close,               mutate_fun = to.monthly,               col_rename = "sse") %>%   mutate(date = as.Date(date)) #Splittingsplit <-   df_shanghai %>%   time_series_split(assess = "1 year",                     cumulative = TRUE)df_train <- training(split)df_test <- testing(split)#Time series cross validation for tuningdf_folds <- time_series_cv(df_train,                           initial = 77,                            assess = 12)#Preprocessing rec <-   recipe(sse ~ date, data = df_train) %>%   step_mutate(date_num = as.numeric(date)) %>%   step_date(date, features = "month") %>%   step_dummy(date_month, one_hot = TRUE) %>%   step_normalize(all_numeric_predictors())rec %>%   prep() %>%   bake(new_data = NULL) %>% view()#Modelmod <-   arima_boost(    min_n = tune(),    learn_rate = tune(),    trees = tune()  ) %>%  set_engine(engine = "auto_arima_xgboost")#Workflow setwflow_mod <-   workflow_set(    preproc = list(rec = rec),    models = list(mod = mod)  ) #Tuning and evaluating the model on all the samplesgrid_ctrl <-  control_grid(    save_pred = TRUE,    parallel_over = "everything",    save_workflow = TRUE  )grid_results <-  wflow_mod %>%  workflow_map(    seed = 98765,    resamples = df_folds,    grid = 10,    control = grid_ctrl  )#Accuracy of the grid resultsgrid_results %>%   rank_results(select_best = TRUE,                rank_metric = "rmse") %>%  select(Models = wflow_id, .metric, mean)#Finalizing the model with the best parametersbest_param <-   grid_results %>%  extract_workflow_set_result("rec_mod") %>%   select_best(metric = "rmse")wflw_fit <-   grid_results %>%   extract_workflow("rec_mod") %>%   finalize_workflow(best_param) %>%   fit(df_train)#Calibrate the model to the testing setcalibration_boost <-   wflw_fit %>%  modeltime_calibrate(new_data = df_test)#Accuracy of the finalized modelcalibration_boost %>%  modeltime_accuracy(metric_set = metric_set(mape, smape))#Predictive intervalscalibration_boost %>%  modeltime_forecast(actual_data = df_merged %>%                        filter(date >= last(date) - months(12)),                     new_data = df_test) %>%  plot_modeltime_forecast(.interactive = FALSE,                          .legend_show = FALSE,                          .line_size = 1.5,                          .color_lab = "",                          .title = "Shanghai Composite Index") +  geom_point(aes(color = .key)) +  labs(subtitle = "Monthly Data<br><span style = 'color:darkgrey;'>Predictive Intervals</span><br><span style = 'color:red;'>Point Forecast Line</span>") +   scale_x_date(breaks = c(make_date(2023,11,1),                           make_date(2024,5,1),                          make_date(2024,10,1)),               labels = scales::label_date(format = "%b'%y"),               expand = expansion(mult = c(.1, .1))) +  ggthemes::theme_wsj(    base_family = "Roboto Slab",    title_family = "Roboto Slab",    color = "blue",    base_size = 12) +  theme(legend.position = "none",        plot.background = element_rect(fill = "lightyellow", color = "lightyellow"),        plot.title = element_text(size = 24),        axis.text = element_text(size = 16),        plot.subtitle = ggtext::element_markdown(size = 20, face = "bold"))