{"id":36056,"date":"2020-02-26T14:18:43","date_gmt":"2020-02-26T19:18:43","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=36056"},"modified":"2022-11-21T09:45:07","modified_gmt":"2022-11-21T14:45:07","slug":"time-series-classification-synthetic-vs-real-financial-time-series","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/time-series-classification-synthetic-vs-real-financial-time-series\/","title":{"rendered":"Time Series Classification Synthetic vs Real Financial Time Series"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Distinguishing between real financial time series and synthetic time series using XGBoost <\/h2>\n\n\n\n<hr class=\"wp-block-separator\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><em>Excerpt<\/em><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"> I was given a \u201c<strong>Data Science<\/strong>\u201d challenge as part of an interview in which I had to distinguish between <strong>real financial time<\/strong> series and <strong>synthetic time series<\/strong>. I document the results here, the data was anonymous and I have no idea which assets were which or from what time series the assets came from. <\/p>\n\n\n\n<hr class=\"wp-block-separator\"\/>\n\n\n\n<p class=\"wp-block-paragraph\">All I knew was that I had 12,000 real time series and 12,000 synthetically created time series. (apologies for no data but this was the companies data and not mine, I have uploaded the <strong>train <\/strong>and <strong>test data sets<\/strong> discussed later&nbsp;<a href=\"https:\/\/github.com\/msmith01\/financial_time_series\">here<\/a>&nbsp;where you should be able to run the final <code>XGBoost <\/code>model). In total there were 24,000 observations. I show the code here for methodological purposes and if you are interested in visualising time series in R and&nbsp;<code>ggplot2<\/code>. The time series features used here are taken from the following papers:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Large Scale Unusual Time Series Detection by R.Hyndman, E.Wang and N.Laptev<\/li><li>Visualising forecasting algorithm performance using time series instance spaces by Y.Kang, Rob.Hyndman and Kate Smith-Miles<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">You can check out my Jupyter Notebook version&nbsp;<a href=\"https:\/\/nbviewer.jupyter.org\/github\/msmith01\/time_series_detection\/blob\/master\/Time_Series_Classification_Financial_Markets.ipynb\">here<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I added a lot of notes to the code throughout the document which might be of additional interest.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Lets get started\u2026<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I often remove all other data in my environment before hand and turn scientific notation off which is what the first 2 lines does. The&nbsp;<code>shhh<\/code>&nbsp;command is useful for Jupyter Notebooks which outputs all the warning messages, adding&nbsp;<code>shhh<\/code>&nbsp;suppresses these warning messaged when loading in the packages. (In R markdown I can set&nbsp;<code>warning = FALSE<\/code>&nbsp;but there is no option on Notebooks. &#8211; that I know of &#8211; )<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>rm(list = ls())\noptions(scipen=999)\nsetwd('C:\/Users\/Matt\/Desktop\/Data Science Challenge')\nshhh &lt;- suppressPackageStartupMessages\n\nshhh(library(dplyr))\nlibrary(readr)\nlibrary(TSrepr)\nlibrary(ggplot2)\nlibrary(data.table)\nlibrary(cluster)\nlibrary(clusterCrit)\nlibrary(fractalrock)\nlibrary(cowplot)\nlibrary(tidyr)\nlibrary(tidyquant)\nlibrary(lmtest)\nlibrary(aTSA)\nlibrary(tsoutliers)\nlibrary(tsfeatures)\nlibrary(xgboost)\nlibrary(caret)\nlibrary(purrr)\n\ntrain_val &lt;- read_csv(\"train.csv\")\ntest &lt;- read_csv(\"test.csv\")<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>NOTE<\/strong>:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I have 2 data sets, the&nbsp;<code>train_Val.csv<\/code>&nbsp;for training and validation data set and the&nbsp;<code>test.csv<\/code>&nbsp;data set. I do not touch the&nbsp;<code>test.csv<\/code>&nbsp;data set until the very end in&nbsp;<em>part 3<\/em>. All the analysis and optimisation is performed only on the&nbsp;<code>train_val.csv<\/code>&nbsp;data set. The&nbsp;<code>train_val.csv<\/code>&nbsp;contains 12,000 observations and the&nbsp;<code>test.csv<\/code>&nbsp;contains 12,000 observations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Part 1<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The data was given to me in this format:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>head(train_val[, 1:5], 1)<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>## # A tibble: 1 x 5\n##   feature1 feature2 feature3 feature4 feature5\n##      &lt;dbl>    &lt;dbl>    &lt;dbl>    &lt;dbl>    &lt;dbl>\n## 1  0.00629  0.00441  -0.0381   0.0253 -0.00658<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The names of the columns are as follows:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>colnames(train_val) %>%\n  data.frame() %>%\n  setNames(c(\"features\")) %>%\n  split(as.integer(gl(nrow(.), 20, nrow(.)))) %>%\n  kable(caption = \"Time series variables\") %>%\n  kable_styling(bootstrap_options = c(\"striped\", \"hover\", \"condensed\", \"responsive\"), font_size = 12)<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The goal<\/strong>: Was to classify which financial time series were&nbsp;<em>real<\/em>&nbsp;vs which were&nbsp;<em>synthetically created<\/em>&nbsp;(by some algorithm I have no knowledge of how it generated the synthetic time series)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I re-arranged the data using the&nbsp;<code>melt<\/code>&nbsp;function in R, however I suggest anybody reading this to use the&nbsp;<code>pivol_longer<\/code>&nbsp;function from the&nbsp;<code>tidyverse<\/code>&nbsp;packages. The&nbsp;<code>pivot_longer<\/code>&nbsp;package was released a few weeks after writing the code for this problem.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Visit Matthew Smith R Blog to read the full article and download the R code:<br><a href=\"https:\/\/lf0.com\/post\/synth-real-time-series\/financial-time-series\/\">https:\/\/lf0.com\/post\/synth-real-time-series\/financial-time-series\/<\/a> <\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Matthew Smith demonstrates how to use select R packages to distinguish between real financial time series and synthetic time series. Ready-to-use R scripts are available for download<\/p>\n","protected":false},"author":372,"featured_media":36372,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,338,341,351,344,342],"tags":[806,6613,6611,6614,852,487,6591,6612,1045,5519,2536],"contributors-categories":[13694],"class_list":["post-36056","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-ibkr-quant-news","category-quant-development","category-quant-europe","category-quant-regions","category-r-development","tag-data-science","tag-financial-data","tag-financial-markets-asset-pricing","tag-jupyter-notebook","tag-machine-learning","tag-r","tag-rstats","tag-synthetic-time-series","tag-tidyverse","tag-time-series","tag-visualization","contributors-categories-matthew-smith-r-blog"],"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 plugin v26.9 (Yoast SEO v28.3) - 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