{"id":80190,"date":"2021-03-17T11:43:40","date_gmt":"2021-03-17T15:43:40","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=80190"},"modified":"2023-02-23T14:22:30","modified_gmt":"2023-02-23T19:22:30","slug":"time-series-classification-synthetic-vs-real-financial-time-series-part-viii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/time-series-classification-synthetic-vs-real-financial-time-series-part-viii\/","title":{"rendered":"Time Series Classification Synthetic vs Real Financial Time Series \u2013 Part VIII"},"content":{"rendered":"\n

Learn which R packages and data sets you need<\/em> by reviewing<\/em> Part I<\/a>, Part II<\/a> ,Part III<\/a><\/em>, Part IV,<\/a> Part V<\/a> , Part VI<\/a> and Part VII<\/a><\/em> of <\/em>this series.<\/em><\/p>\n\n\n\n

That’s enough data analysis I could probably fit the PACF plots also along with a few more exploratory data analysis but I move on to generating the financial time series features using the tsfeatures<\/code> package.<\/p>\n\n\n\n

What I do in the below code is to take a random sample of 5 groups (Using the whole data set takes too long to calculate the time series features) and then apply all the functions in the tsfeatures<\/code> package to each of the time series assets data which is does by mapping over each assets data and computing the time series features.<\/p>\n\n\n\n

This section takes some time to process and compute (especially on the whole sample) and I already saved the results as a csv which I will just work from and load in the pre-computed time series features.<\/p>\n\n\n\n

\n################# Generate Time Series Features ######################

\n# I create some time series features from the package “tsfeatures”. There are 40+ functions in the “tsfeatures” package
\n# which can generate approximately 106 time series features.
\n# Due to memory issues I am only able to create a few of the features, therefore I randomly sample 10 features from the
\n# “tsfeatures” package. We could also add in technical indicators from the “PerformanceAnalytics” or “TTR” packages (I omit these
\n# here, however creating ‘functions2 <- ls(\"package:TTR\")' and adding it to the 'summarise' command will work.)

\nfunctions <- ls(\"package:tsfeatures\")[1:42]
\n# functions <- sample(functions, 20)

\nStats <- df %>%
\n group_by(row_id, class) %>%
\n nest() %>%
\n ungroup() %>%
\n sample_n(5) %>%
\n unnest() %>%
\n nest(-row_id, -class) %>%
\n group_by(row_id, class) %T>%
\n {options(warn = -1)} %>%
\n summarise(Statistics = map(data, ~ data.frame(
\n bind_cols(
\n tsfeatures(.x$value, functions))))) %>%
\n unnest(Statistics)\n<\/p>\n\n\n\n

\n# I saved to whole dataset as “Stats” next I split it between training and test.
\nStats <- read.csv(\"C:\/Users\/Matt\/Desktop\/Data Science Challenge\/TSfeatures_train_val.csv\")\n<\/p>\n\n\n\n

Note:<\/strong> Again, bad practice by me. I just called the df<\/code> data Stats<\/code> which consists of only the time series features. This still only refers to the train_val.csv<\/code> data and not<\/em> the test.csv<\/code> data.<\/p>\n\n\n\n

The training data looks like: (after computing the time series features). Now each asset has been collapsed from ~260 days down to 1 signal time series feature observation.<\/p>\n\n\n\n

Recall the goal here was to classify synthetic time series vs real time series and not what the next days price is going to be. For each asset I have a signal observation and based on this I can train a classifying algorithm to distinguish between real vs synthetic time series.<\/p>\n\n\n\n

How the training data looks:<\/h3>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\nTable 4: <\/span>tsfeatures package features\n<\/caption>\n
\nX\n<\/th>\n\nrow_id\n<\/th>\n\nclass\n<\/th>\n\nac_9_ac_9\n<\/th>\n\nacf_features_x_acf1\n<\/th>\n\nacf_features_x_acf10\n<\/th>\n\nacf_features_diff1_acf1\n<\/th>\n\nacf_features_diff1_acf10\n<\/th>\n\nacf_features_diff2_acf1\n<\/th>\n\nacf_features_diff2_acf10\n<\/th>\n\nARCH.LM\n<\/th>\n\nautocorr_features_embed2_incircle_1\n<\/th>\n\nautocorr_features_embed2_incircle_2\n<\/th>\n\nautocorr_features_ac_9\n<\/th>\n\nautocorr_features_firstmin_ac\n<\/th>\n\nautocorr_features_trev_num\n<\/th>\n\nautocorr_features_motiftwo_entro3\n<\/th>\n\nautocorr_features_walker_propcross\n<\/th>\n\nbinarize_mean_binarize_mean\n<\/th>\n\nbinarize_mean_NA\n<\/th>\n\ncompengine_embed2_incircle_1\n<\/th>\n\ncompengine_embed2_incircle_2\n<\/th>\n\ncompengine_ac_9\n<\/th>\n\ncompengine_firstmin_ac\n<\/th>\n\ncompengine_trev_num\n<\/th>\n\ncompengine_motiftwo_entro3\n<\/th>\n\ncompengine_walker_propcross\n<\/th>\n\ncompengine_localsimple_mean1\n<\/th>\n\ncompengine_localsimple_lfitac\n<\/th>\n\ncompengine_sampen_first\n<\/th>\n\ncompengine_std1st_der\n<\/th>\n\ncompengine_spreadrandomlocal_meantaul_50\n<\/th>\n\ncompengine_spreadrandomlocal_meantaul_ac2\n<\/th>\n\ncompengine_histogram_mode_10\n<\/th>\n\ncompengine_outlierinclude_mdrmd\n<\/th>\n\ncompengine_fluctanal_prop_r1\n<\/th>\n\ncrossing_points\n<\/th>\n\ndist_features_histogram_mode_10\n<\/th>\n\ndist_features_outlierinclude_mdrmd\n<\/th>\n\nembed2_incircle\n<\/th>\n\nentropy\n<\/th>\n\nfirstmin_ac\n<\/th>\n\nfirstzero_ac\n<\/th>\n\nflat_spots\n<\/th>\n\nfluctanal_prop_r1_fluctanal_prop_r1\n<\/th>\n\narch_acf\n<\/th>\n\ngarch_acf\n<\/th>\n\narch_r2\n<\/th>\n\ngarch_r2\n<\/th>\n\nhistogram_mode\n<\/th>\n\nalpha\n<\/th>\n\nbeta\n<\/th>\n\nhurst\n<\/th>\n\nhw_parameters_hw_parameters\n<\/th>\n\nhw_parameters_NA\n<\/th>\n\nlocalsimple_taures\n<\/th>\n\nlumpiness\n<\/th>\n\nmax_kl_shift\n<\/th>\n\ntime_kl_shift\n<\/th>\n\nmax_level_shift\n<\/th>\n\ntime_level_shift\n<\/th>\n\nmax_var_shift\n<\/th>\n\ntime_var_shift\n<\/th>\n\nmotiftwo_entro3\n<\/th>\n\nnonlinearity\n<\/th>\n\noutlierinclude_mdrmd\n<\/th>\n\nx_pacf5\n<\/th>\n\ndiff1x_pacf5\n<\/th>\n\ndiff2x_pacf5\n<\/th>\n\npred_features_localsimple_mean1\n<\/th>\n\npred_features_localsimple_lfitac\n<\/th>\n\npred_features_sampen_first\n<\/th>\n\nsampen_first_sampen_first\n<\/th>\n\nsampenc\n<\/th>\n\nscal_features_fluctanal_prop_r1\n<\/th>\n\nspreadrandomlocal_meantaul\n<\/th>\n\nstability\n<\/th>\n\nstation_features_std1st_der\n<\/th>\n\nstation_features_spreadrandomlocal_meantaul_50\n<\/th>\n\nstation_features_spreadrandomlocal_meantaul_ac2\n<\/th>\n\nstd1st_der_std1st_der\n<\/th>\n\nnperiods\n<\/th>\n\nseasonal_period\n<\/th>\n\ntrend\n<\/th>\n\nspike\n<\/th>\n\nlinearity\n<\/th>\n\ncurvature\n<\/th>\n\ne_acf1\n<\/th>\n\ne_acf10\n<\/th>\n\ntrev_num\n<\/th>\n\ntsfeatures_frequency\n<\/th>\n\ntsfeatures_nperiods\n<\/th>\n\ntsfeatures_seasonal_period\n<\/th>\n\ntsfeatures_trend\n<\/th>\n\ntsfeatures_spike\n<\/th>\n\ntsfeatures_linearity\n<\/th>\n\ntsfeatures_curvature\n<\/th>\n\ntsfeatures_e_acf1\n<\/th>\n\ntsfeatures_e_acf10\n<\/th>\n\ntsfeatures_entropy\n<\/th>\n\ntsfeatures_x_acf1\n<\/th>\n\ntsfeatures_x_acf10\n<\/th>\n\ntsfeatures_diff1_acf1\n<\/th>\n\ntsfeatures_diff1_acf10\n<\/th>\n\ntsfeatures_diff2_acf1\n<\/th>\n\ntsfeatures_diff2_acf10\n<\/th>\n\nunitroot_kpss\n<\/th>\n\nunitroot_pp\n<\/th>\n\nwalker_propcross\n<\/th>\n<\/tr>\n<\/thead>\n
\n1\n<\/td>\n\n1\n<\/td>\n\n0\n<\/td>\n\n-0.0675275\n<\/td>\n\n0.0097094\n<\/td>\n\n0.0526897\n<\/td>\n\n-0.5005299\n<\/td>\n\n0.3297018\n<\/td>\n\n-0.6772403\n<\/td>\n\n0.6124739\n<\/td>\n\n0.0627825\n<\/td>\n\n0.3929961\n<\/td>\n\n0.6147860\n<\/td>\n\n-0.0675275\n<\/td>\n\n1\n<\/td>\n\n0.1208750\n<\/td>\n\n2.071663\n<\/td>\n\n0.5405405\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n0.3929961\n<\/td>\n\n0.6147860\n<\/td>\n\n-0.0675275\n<\/td>\n\n1\n<\/td>\n\n0.1208750\n<\/td>\n\n2.071663\n<\/td>\n\n0.5405405\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n1.788841\n<\/td>\n\n1.408737\n<\/td>\n\n1.68\n<\/td>\n\n1.43\n<\/td>\n\n-0.25\n<\/td>\n\n-0.2865385\n<\/td>\n\n0.1627907\n<\/td>\n\n132\n<\/td>\n\n-0.25\n<\/td>\n\n-0.2865385\n<\/td>\n\n0.3929961\n<\/td>\n\n0.9840151\n<\/td>\n\n1\n<\/td>\n\n3\n<\/td>\n\n4\n<\/td>\n\n0.1627907\n<\/td>\n\n0.0652585\n<\/td>\n\n0.0154406\n<\/td>\n\n0.0627825\n<\/td>\n\n0.0253367\n<\/td>\n\n-0.25\n<\/td>\n\n0.0013330\n<\/td>\n\n0.0013330\n<\/td>\n\n0.5000458\n<\/td>\n\nNA\n<\/td>\n\nNA\n<\/td>\n\n1\n<\/td>\n\n0.3556536\n<\/td>\n\n1.783636\n<\/td>\n\n103\n<\/td>\n\n1.297736\n<\/td>\n\n97\n<\/td>\n\n2.819828\n<\/td>\n\n46\n<\/td>\n\n2.071663\n<\/td>\n\n0.0752319\n<\/td>\n\n-0.2865385\n<\/td>\n\n0.0108653\n<\/td>\n\n0.4457792\n<\/td>\n\n1.0525222\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n1.788841\n<\/td>\n\n1.788841\n<\/td>\n\n1.788841\n<\/td>\n\n0.1627907\n<\/td>\n\n1.76\n<\/td>\n\n0.0562693\n<\/td>\n\n1.408737\n<\/td>\n\n1.74\n<\/td>\n\n1.36\n<\/td>\n\n1.408737\n<\/td>\n\n0\n<\/td>\n\n1\n<\/td>\n\n0.0043052\n<\/td>\n\n0.0000261\n<\/td>\n\n0.8421403\n<\/td>\n\n-0.7069160\n<\/td>\n\n0.0052389\n<\/td>\n\n0.0588324\n<\/td>\n\n0.1208750\n<\/td>\n\n1\n<\/td>\n\n0\n<\/td>\n\n1\n<\/td>\n\n0.0043052\n<\/td>\n\n0.0000261\n<\/td>\n\n0.8421403\n<\/td>\n\n-0.7069160\n<\/td>\n\n0.0052389\n<\/td>\n\n0.0588324\n<\/td>\n\n0.9840151\n<\/td>\n\n0.0097094\n<\/td>\n\n0.0526897\n<\/td>\n\n-0.5005299\n<\/td>\n\n0.3297018\n<\/td>\n\n-0.6772403\n<\/td>\n\n0.6124739\n<\/td>\n\n0.0993829\n<\/td>\n\n-249.7732\n<\/td>\n\n0.5405405\n<\/td>\n<\/tr>\n
\n2\n<\/td>\n\n2\n<\/td>\n\n0\n<\/td>\n\n-0.0421577\n<\/td>\n\n-0.0075902\n<\/td>\n\n0.0387481\n<\/td>\n\n-0.5171529\n<\/td>\n\n0.3129147\n<\/td>\n\n-0.6727897\n<\/td>\n\n0.5379301\n<\/td>\n\n0.0558032\n<\/td>\n\n0.4285714\n<\/td>\n\n0.6563707\n<\/td>\n\n-0.0421577\n<\/td>\n\n1\n<\/td>\n\n-0.4765229\n<\/td>\n\n2.077581\n<\/td>\n\n0.5019305\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n0.4285714\n<\/td>\n\n0.6563707\n<\/td>\n\n-0.0421577\n<\/td>\n\n1\n<\/td>\n\n-0.4765229\n<\/td>\n\n2.077581\n<\/td>\n\n0.5019305\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n1.780390\n<\/td>\n\n1.419266\n<\/td>\n\n1.95\n<\/td>\n\n1.00\n<\/td>\n\n0.50\n<\/td>\n\n0.2615385\n<\/td>\n\n0.1627907\n<\/td>\n\n123\n<\/td>\n\n0.50\n<\/td>\n\n0.2615385\n<\/td>\n\n0.4285714\n<\/td>\n\n0.9864332\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n4\n<\/td>\n\n0.1627907\n<\/td>\n\n0.0664358\n<\/td>\n\n0.0657859\n<\/td>\n\n0.0558032\n<\/td>\n\n0.0554355\n<\/td>\n\n0.50\n<\/td>\n\n0.0001000\n<\/td>\n\n0.0001000\n<\/td>\n\n0.5000458\n<\/td>\n\nNA\n<\/td>\n\nNA\n<\/td>\n\n1\n<\/td>\n\n0.4636768\n<\/td>\n\n1.733008\n<\/td>\n\n247\n<\/td>\n\n1.311861\n<\/td>\n\n141\n<\/td>\n\n2.625772\n<\/td>\n\n221\n<\/td>\n\n2.077581\n<\/td>\n\n0.0273335\n<\/td>\n\n0.2615385\n<\/td>\n\n0.0256032\n<\/td>\n\n0.4606850\n<\/td>\n\n1.0171377\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n1.780390\n<\/td>\n\n1.780390\n<\/td>\n\n1.780390\n<\/td>\n\n0.1627907\n<\/td>\n\n2.05\n<\/td>\n\n0.0892206\n<\/td>\n\n1.419266\n<\/td>\n\n2.12\n<\/td>\n\n1.00\n<\/td>\n\n1.419266\n<\/td>\n\n0\n<\/td>\n\n1\n<\/td>\n\n0.0177460\n<\/td>\n\n0.0000399\n<\/td>\n\n0.9249561\n<\/td>\n\n0.7665407\n<\/td>\n\n-0.0218053\n<\/td>\n\n0.0411861\n<\/td>\n\n-0.4765229\n<\/td>\n\n1\n<\/td>\n\n0\n<\/td>\n\n1\n<\/td>\n\n0.0177460\n<\/td>\n\n0.0000399\n<\/td>\n\n0.9249561\n<\/td>\n\n0.7665407\n<\/td>\n\n-0.0218053\n<\/td>\n\n0.0411861\n<\/td>\n\n0.9864332\n<\/td>\n\n-0.0075902\n<\/td>\n\n0.0387481\n<\/td>\n\n-0.5171529\n<\/td>\n\n0.3129147\n<\/td>\n\n-0.6727897\n<\/td>\n\n0.5379301\n<\/td>\n\n0.0414599\n<\/td>\n\n-256.0485\n<\/td>\n\n0.5019305\n<\/td>\n<\/tr>\n
\n3\n<\/td>\n\n3\n<\/td>\n\n1\n<\/td>\n\n0.0099598\n<\/td>\n\n-0.0405929\n<\/td>\n\n0.0449036\n<\/td>\n\n-0.5026683\n<\/td>\n\n0.3471209\n<\/td>\n\n-0.6718885\n<\/td>\n\n0.6109006\n<\/td>\n\n0.0325470\n<\/td>\n\n0.4671815\n<\/td>\n\n0.7065637\n<\/td>\n\n0.0099598\n<\/td>\n\n1\n<\/td>\n\n-0.8755173\n<\/td>\n\n2.069233\n<\/td>\n\n0.5328185\n<\/td>\n\n1\n<\/td>\n\n0\n<\/td>\n\n0.4671815\n<\/td>\n\n0.7065637\n<\/td>\n\n0.0099598\n<\/td>\n\n1\n<\/td>\n\n-0.8755173\n<\/td>\n\n2.069233\n<\/td>\n\n0.5328185\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n1.706841\n<\/td>\n\n1.443315\n<\/td>\n\n1.38\n<\/td>\n\n1.00\n<\/td>\n\n-0.50\n<\/td>\n\n-0.2538462\n<\/td>\n\n0.1395349\n<\/td>\n\n132\n<\/td>\n\n-0.50\n<\/td>\n\n-0.2538462\n<\/td>\n\n0.4671815\n<\/td>\n\n0.9868568\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n6\n<\/td>\n\n0.1395349\n<\/td>\n\n0.0388513\n<\/td>\n\n0.0039162\n<\/td>\n\n0.0325470\n<\/td>\n\n0.0041902\n<\/td>\n\n-0.50\n<\/td>\n\n0.0014557\n<\/td>\n\n0.0014557\n<\/td>\n\n0.5000458\n<\/td>\n\nNA\n<\/td>\n\nNA\n<\/td>\n\n1\n<\/td>\n\n1.2670493\n<\/td>\n\n7.746711\n<\/td>\n\n95\n<\/td>\n\n1.403784\n<\/td>\n\n87\n<\/td>\n\n5.235499\n<\/td>\n\n84\n<\/td>\n\n2.069233\n<\/td>\n\n0.2436499\n<\/td>\n\n-0.2538462\n<\/td>\n\n0.0223069\n<\/td>\n\n0.5356408\n<\/td>\n\n0.9954919\n<\/td>\n\n1\n<\/td>\n\n1\n<\/td>\n\n1.706841\n<\/td>\n\n1.706841\n<\/td>\n\n1.706841\n<\/td>\n\n0.1395349\n<\/td>\n\n1.42\n<\/td>\n\n0.0716499\n<\/td>\n\n1.443315\n<\/td>\n\n1.42\n<\/td>\n\n1.00\n<\/td>\n\n1.443315\n<\/td>\n\n0\n<\/td>\n\n1\n<\/td>\n\n0.0141368\n<\/td>\n\n0.0000929\n<\/td>\n\n0.8414359\n<\/td>\n\n-0.0259311\n<\/td>\n\n-0.0547484\n<\/td>\n\n0.0492987\n<\/td>\n\n-0.8755173\n<\/td>\n\n1\n<\/td>\n\n0\n<\/td>\n\n1\n<\/td>\n\n0.0141368\n<\/td>\n\n0.0000929\n<\/td>\n\n0.8414359\n<\/td>\n\n-0.0259311\n<\/td>\n\n-0.0547484\n<\/td>\n\n0.0492987\n<\/td>\n\n0.9868568\n<\/td>\n\n-0.0405929\n<\/td>\n\n0.0449036\n<\/td>\n\n-0.5026683\n<\/td>\n\n0.3471209\n<\/td>\n\n-0.6718885\n<\/td>\n\n0.6109006\n<\/td>\n\n0.0775698\n<\/td>\n\n-258.1295\n<\/td>\n\n0.5328185\n<\/td>\n<\/tr>\n
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## [1] 12000 109<\/p>\n\n\n\n

The dimensions of the data as still 12,000 with 109 features (created from the tsfeatures package). That is we have 6,000 synthetic and 6,000 real financial time series (12,000 * ~260 = 3,120,000 but we applied tsfeatures to collapse the ~260 down to 1 single observation for each asset)<\/p>\n\n\n\n

I collapsed this problem down from a time series expectation problem to a pure classification problem. I split the data between training and validation set next\u2026 I also split the data into X_train<\/code>, Y_train<\/code>\u2026 etc.<\/p>\n\n\n\n

I split the df\/Stats<\/code> data set into a train set of 75% of the observations and an in-sample<\/em> test data set of 25% of the observations.<\/p>\n\n\n\n

\n######################################################################
\n################# Train and XGBoost model on the TS Features #########

\n#Stats <- Stats %>%
\n# select_if(~sum(!is.na(.)) > 0)

\n# Split the training set up between train and a small validation set
\nsmp_size <- floor(0.75 * nrow(Stats))
\n#set.seed(123)
\ntrain_ind <- sample(seq_len(nrow(Stats)), size = smp_size)

\ntrain <- Stats[train_ind, ]
\nval <- Stats[-train_ind, ]
\n# We have 106 time series features for the model to learn from.

\nx_train <- train %>%
\n ungroup() %>%
\n select(-class, -row_id, -X) %>%
\n as.matrix()

\nx_val <- val %>%
\n ungroup() %>%
\n select(-class, -row_id, -X) %>%
\n as.matrix()

\ny_train <- train %>%
\n ungroup() %>%
\n pull(class)
\ny_val <- val %>%
\n ungroup() %>%
\n pull(class)\n<\/p>\n\n\n\n

Stay tuned for the next installment to find out how the training X (input variables) data looks.<\/em><\/p>\n\n\n\n

Visit Matthew Smith \u2013 R Blog to download the complete R code and see additional details featured in this tutorial: https:\/\/lf0.com\/post\/synth-real-time-series\/financial-time-series\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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