{"id":58431,"date":"2020-09-03T10:20:44","date_gmt":"2020-09-03T14:20:44","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=58431"},"modified":"2022-11-21T09:46:14","modified_gmt":"2022-11-21T14:46:14","slug":"time-series-classification-synthetic-vs-real-financial-time-series-part-v","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/time-series-classification-synthetic-vs-real-financial-time-series-part-v\/","title":{"rendered":"Time Series Classification Synthetic vs Real Financial Time Series \u2013 Part V"},"content":{"rendered":"\n

See Part I<\/a>, Part II<\/a> , <\/em>Part III<\/a><\/em> and Part IV<\/a> in this article for instructions from Matthew Smith on which R packages and data sets you need.<\/em><\/p>\n\n\n\n

An important note in the code here is that I randomly sample by group<\/em>, that is, I do not take a random sample from all observations across all groups. Instead I group_by<\/code> each time series (each of the 6,000 observations after I filtered by the class == 0<\/code>, likewise when I filter by the class == 1<\/code>), I then nest()<\/code> the data to collapse the daily time series for each asset into a list<\/code>. From here I will have 6,000 observations, each of which has their time series nested inside a list. Thus, I can sample 1 of the 6,000 observations and then unnest()<\/code> and obtain a full time series set of one of the random assets selected, – instead of sampling randomly over all assets time series data (which would be completely wrong).<\/p>\n\n\n\n

For example the following commented out code group_by()<\/code> the ID variable and nest()<\/code> the data, takes a random sample_n()<\/code> of the grouped data and then unnest()<\/code> the data to its original form, this time with a random sample of the IDs.<\/p>\n\n\n\n

\n# group_by(row_id) %>%
\n# nest() %>%
\n# ungroup() %>%
\n# sample_n(1) %>%
\n# unnest() %>%\n<\/p>\n\n\n\n

Next I compute the Dickey Fuller test on both series for a single random observation, hence the sample_n(1)<\/code> argument (it\u2019s too computationally expensive to compute it on all 12,000 observations).<\/p>\n\n\n\n

For the synthetically created series.<\/p>\n\n\n\n

\n# Dickey Fuller test on the 0 class
\n# I only randomly sample 1 of the assets for the 0 class to save on output space

\n\ndf %>%
\n filter(class == 0) %>%
\n group_by(row_id) %>%
\n nest() %>%
\n ungroup() %>%
\n sample_n(1) %>%
\n unnest() %>%
\n nest(-row_id) %>%
\n mutate(adf_res = map(data, ~ adf.test(.x$value))) %>%
\n unnest(adf_res)\n<\/p>\n\n\n\n

\n## Augmented Dickey-Fuller Test
\n## alternative: stationary
\n## \n## Type 1: no drift no trend
\n## lag ADF p.value
\n## [1,] 0 -17.94 0.01
\n## [2,] 1 -11.75 0.01
\n## [3,] 2 -8.66 0.01
\n## [4,] 3 -7.62 0.01
\n## [5,] 4 -7.13 0.01
\n## Type 2: with drift no trend
\n## lag ADF p.value
\n## [1,] 0 -17.94 0.01
\n## [2,] 1 -11.76 0.01
\n## [3,] 2 -8.67 0.01
\n## [4,] 3 -7.64 0.01
\n## [5,] 4 -7.15 0.01
\n## Type 3: with drift and trend
\n## lag ADF p.value
\n## [1,] 0 -18.00 0.01
\n## [2,] 1 -11.83 0.01
\n## [3,] 2 -8.77 0.01
\n## [4,] 3 -7.74 0.01
\n## [5,] 4 -7.26 0.01
\n## —-
\n## Note: in fact, p.value = 0.01 means p.value <= 0.01\n<\/p>\n\n\n\n

\n## # A tibble: 3 x 3
\n## row_id data adf_res
\n## >
\n## 1 7807 [260 x 4]
\n## 2 7807 [260 x 4]
\n## 3 7807 [260 x 4] \n<\/p>\n\n\n\n

The same but on the real financial series.<\/p>\n\n\n\n

\n# Dickey Fuller test on the 1 class
\n# I only randomly sample 1 of the assets for the 1 class to save on output space

\n\ndf %>%
\n filter(class == 1) %>%
\n group_by(row_id) %>%
\n nest() %>%
\n ungroup() %>%
\n sample_n(1) %>%
\n unnest() %>%
\n nest(-row_id) %>%
\n mutate(adf_res = map(data, ~ adf.test(.x$value))) %>%
\n unnest(adf_res)\n<\/p>\n\n\n\n

\n## Augmented Dickey-Fuller Test
\n## alternative: stationary
\n##
\n## Type 1: no drift no trend
\n## lag ADF p.value
\n## [1,] 0 -15.99 0.01
\n## [2,] 1 -10.71 0.01
\n## [3,] 2 -9.12 0.01
\n## [4,] 3 -8.74 0.01
\n## [5,] 4 -7.58 0.01
\n## Type 2: with drift no trend
\n## lag ADF p.value
\n## [1,] 0 -16.10 0.01
\n## [2,] 1 -10.84 0.01
\n## [3,] 2 -9.27 0.01
\n## [4,] 3 -8.93 0.01
\n## [5,] 4 -7.81 0.01
\n## Type 3: with drift and trend
\n## lag ADF p.value
\n## [1,] 0 -16.27 0.01
\n## [2,] 1 -10.99 0.01
\n## [3,] 2 -9.46 0.01
\n## [4,] 3 -9.18 0.01
\n## [5,] 4 -8.06 0.01
\n## —-
\n## Note: in fact, p.value = 0.01 means p.value <= 0.01\n<\/p>\n\n\n\n

\n## # A tibble: 3 x 3
\n## row_id data adf_res
\n## >
\n## 1 10833 [260 x 4]
\n## 2 10833 [260 x 4]
\n## 3 10833 [260 x 4] \n<\/p>\n\n\n\n

Stay tuned for the next installment, in which Matthew will review he Jarque-Bera tests for normality.<\/em><\/p>\n\n\n\n

Visit Matthew Smith \u2013 R Blog<\/a> to see the next step in his analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"

Matthew Smith computes the Dickey Fuller test on synthetically created series and real financial series.<\/p>\n","protected":false},"author":372,"featured_media":58432,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,338,341,351,344,342],"tags":[6989,8343,806,8344,8342,6613,6988,6614,852,7811,1044,1045,5519,2536],"contributors-categories":[13694],"class_list":{"0":"post-58431","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-ibkr-quant-news","10":"category-quant-development","11":"category-quant-europe","12":"category-quant-regions","13":"category-r-development","14":"tag-asset-pricing","15":"tag-box-plot","16":"tag-data-science","17":"tag-dickey-fuller-test","18":"tag-durbin-watson","19":"tag-financial-data","20":"tag-financial-markets","21":"tag-jupyter-notebook","22":"tag-machine-learning","23":"tag-r-rstats","24":"tag-tidyquant","25":"tag-tidyverse","26":"tag-time-series","27":"tag-visualization","28":"contributors-categories-matthew-smith-r-blog"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nTime 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