{"id":243097,"date":"2026-05-20T11:43:05","date_gmt":"2026-05-20T15:43:05","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=243097"},"modified":"2026-05-20T11:44:16","modified_gmt":"2026-05-20T15:44:16","slug":"augmented-dynamic-adaptive-model-adam-for-daily-seasonal-data","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/augmented-dynamic-adaptive-model-adam-for-daily-seasonal-data\/","title":{"rendered":"Augmented Dynamic Adaptive Model (ADAM) for Daily Seasonal\u00a0Data"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>The article &#8220;Augmented Dynamic Adaptive Model (ADAM) for Daily Seasonal Data&#8221; was originally published on <a href=\"https:\/\/datageeek.com\/2025\/09\/18\/augmented-dynamic-adaptive-model-adam-for-daily-seasonal-data\/\">DataGeeek<\/a> blog.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I have modeled the BIST 100 index to build predictive intervals. Because the data has daily seasonality, I preferred the&nbsp;<strong><em>modeltime::adam_reg<\/em><\/strong>&nbsp;function.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I did not use the&nbsp;<strong><em>timetk::step_timeseries_signature<\/em><\/strong>&nbsp;function because the model cannot process too many exterior regressors, and the algorithm captures the trend and seasonality well by nature. So I did not preprocess the data to keep it simple.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"r\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">library(tidyverse)\nlibrary(tidyquant)\nlibrary(tidymodels)\nlibrary(timetk)\nlibrary(modeltime)\n \n#BIST 100\ndf_bist &lt;- \n  tq_get(\"XU100.IS\") %&gt;% \n  select(date, close)\n \n \n#Splitting the Data \nsplits &lt;- \n  time_series_split(\n    df_bist,\n    assess     = \"1 month\",\n    cumulative = TRUE\n  )\n \ndf_train &lt;- training(splits)\ndf_test &lt;- testing(splits)\n \n \n#Seasonality Diagnostic\narima_reg() %&gt;% \n  set_engine(\"auto_arima\") %&gt;% \n  fit(close ~ date, df_train)\n \n \n#Model\nmod_adam &lt;- \n  adam_reg() %&gt;% \n  set_engine(\"auto_adam\")\n \n#Fitting\nmod_fit &lt;- \n  mod_adam %&gt;% \n  fit(formula = close ~ date, data = df_train)\n \n#Calibrate the model to the testing set\ncalibration_tbl &lt;- \n  mod_fit %&gt;%\n  modeltime_calibrate(new_data = df_test)\n \n \n#Accuracy of the finalized model\ncalibration_tbl %&gt;%\n  modeltime_accuracy(metric_set = metric_set(rmse, rsq, mape))\n \n \n \n#Prediction Intervals\ncalibration_tbl %&gt;% \n  modeltime_forecast(new_data = df_test, \n                     actual_data = df_test) %&gt;%\n  plot_modeltime_forecast(.interactive = FALSE,\n                          .legend_show = FALSE,\n                          .line_size = 1.5,\n                          .color_lab = \"\",\n                          .title = \"BIST 100\") +\n  labs(subtitle = \"&lt;span style = 'color:dimgrey;'&gt;Predictive Intervals&lt;\/span&gt; of the &lt;span style = 'color:red;'&gt;Augmented Dynamic Adaptive Model&lt;\/span&gt;\") + \n  scale_y_continuous(labels = scales::label_currency(prefix = \"\u20ba\", \n                                                     suffix = \"\")) +\n  scale_x_date(labels = scales::label_date(\"%b %d\"),\n               date_breaks = \"4 days\") +\n  theme_minimal(base_family = \"Roboto Slab\", base_size = 16) +\n  theme(plot.subtitle = ggtext::element_markdown(face = \"bold\", size = 14),\n        plot.title = element_text(face = \"bold\"),\n        plot.background = element_rect(fill = \"azure\", color = \"azure\"),\n        panel.background = element_rect(fill = \"snow\", color = \"snow\"),\n        axis.text = element_text(face = \"bold\"),\n        axis.text.x = element_text(angle = 45, \n                                   hjust = 1, \n                                   vjust = 1),\n        legend.position = \"none\")<\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"767\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/05\/bist_datageek-1100x767.png\" alt=\"Augmented Dynamic Adaptive Model\" class=\"wp-image-243100 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/05\/bist_datageek-1100x767.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/05\/bist_datageek-700x488.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/05\/bist_datageek-300x209.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/05\/bist_datageek-768x535.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/05\/bist_datageek.png 1109w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/767;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Source: Yahoo Finance<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I have modeled the BIST 100 index to build predictive intervals.<\/p>\n","protected":false},"author":1729,"featured_media":231564,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,338,341,342],"tags":[21576,9337,806,21575,487,21574,1044,1045,1046],"contributors-categories":[21034],"class_list":["post-243097","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-ibkr-quant-news","category-quant-development","category-r-development","tag-augmented-dynamic-adaptive-model","tag-data-modeling","tag-data-science","tag-modeltime","tag-r","tag-tidymodels","tag-tidyquant","tag-tidyverse","tag-timetk","contributors-categories-datageeek"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium 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