{"id":64366,"date":"2020-10-28T11:30:38","date_gmt":"2020-10-28T15:30:38","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=64366"},"modified":"2022-11-21T09:46:31","modified_gmt":"2022-11-21T14:46:31","slug":"time-series-classification-synthetic-vs-real-financial-time-series-part-vi","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/time-series-classification-synthetic-vs-real-financial-time-series-part-vi\/","title":{"rendered":"Time Series Classification Synthetic vs Real Financial Time Series \u2013 Part VI"},"content":{"rendered":"\n

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

Next the Jarque-Bera tests for normality. Firstly on the synthetically created series.<\/p>\n\n\n\n

\t\n# For both classes I take a random sample of 1 observation from each class (Synthetic and Real financial series)

\n\njb_zero <- df %>%
\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(JarqueBeraTest = map(data, ~ JarqueBera.test(.x$value)))

\n\nprint(“Jarque-Bera Test on the 0 – Synthetic class”<\/span>)\n<\/p>\n\n\n\n

\t\n## [1] “Jarque-Bera Test on the 0 – Synthetic class”\n<\/p>\n\n\n\n

\t\njb_zero$JarqueBeraTest\n\n<\/p>\n\n\n\n

\t\n## [[1]]
\n##
\n## Jarque Bera Test
\n##
\n## data: .x$value
\n## X-squared = 0.3088, df = 2, p-value = 0.8569
\n##
\n##
\n## Skewness
\n##
\n## data: .x$value
\n## statistic = 0.045794, p-value = 0.7631
\n##
\n##
\n## Kurtosis
\n##
\n## data: .x$value
\n## statistic = 2.8582, p-value = 0.6406\n\n<\/p>\n\n\n\n

Also on the real financial series.<\/p>\n\n\n\n

\t\njb_one <- df %>%
\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(JarqueBeraTest = map(data, ~ JarqueBera.test(.x$value)))

\n\nprint(“Jarque-Bera Test on the 1 – Real class”<\/span>)\n\n<\/p>\n\n\n\n

\t\n## [1] “Jarque-Bera Test on the 1 – Real class”\n\n\n<\/p>\n\n\n\n

\t\njb_one$JarqueBeraTest\n\n\n\n<\/p>\n\n\n\n

\t\n## [[1]]
\n##
\n## Jarque Bera Test
\n##
\n## data: .x$value
\n## X-squared = 25.14, df = 2, p-value = 0.000003474
\n##
\n##
\n## Skewness
\n##
\n## data: .x$value
\n## statistic = 0.084191, p-value = 0.5794
\n##
\n##
\n## Kurtosis
\n##
\n## data: .x$value
\n## statistic = 4.514, p-value = 0.0000006251
\n\n<\/p>\n\n\n\n

Stay tuned for the next installment for instructions on Autocorrelation plots.<\/em><\/p>\n\n\n\n

Visit Matthew Smith \u2013 R Blog<\/a> to download the complete R code and see the next step in this tutorial.<\/p>\n","protected":false},"excerpt":{"rendered":"

Learn how to run Jarque-Bera tests for normality. 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