{"id":95046,"date":"2021-07-15T11:26:03","date_gmt":"2021-07-15T15:26:03","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=95046"},"modified":"2023-02-28T10:11:22","modified_gmt":"2023-02-28T15:11:22","slug":"time-series-classification-synthetic-vs-real-financial-time-series-part-x","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/time-series-classification-synthetic-vs-real-financial-time-series-part-x\/","title":{"rendered":"Time Series Classification Synthetic vs Real Financial Time Series \u2013 Part X"},"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>, Part VII<\/a><\/em>, Part VIII<\/a> and Part IX<\/a> of this series.<\/em><\/p>\n\n\n\n

Plug the optimal parameters into the model.<\/h2>\n\n\n\n

#################################################################################\n################# XGBoost Optimal Parameters from Cross Validation ##############

\n\n# This is the final training model where I use the most optimal parameters found over the grid space and plug them in here.

\n\nwatchlist <- list(“train” = dtrain)

\n\nparams <- list(“eta” = 0.1, “max_depth” = 5, “colsample_bytree” = 1, “min_child_weight” = 1, “subsample”= 1,\n
                         “objective”=”binary:logistic”, “gamma” = 1, “lambda” = 1, “alpha” = 0, “max_delta_step” = 0,\n
                         “colsample_bylevel” = 1, “eval_metric”= “auc”,\n
                         “set.seed” = 176)

\n\nnround <- 95<\/p>\n\n\n\n

Now that I have the optimal parameters from the cross validation grid search I can train the final XGBoost model on the whole train_val.csv<\/code> data set. (Whereas before the optimal parameters were obtained from different folds<\/em> in the model. More info on k-fold cross validation here<\/a>)<\/p>\n\n\n\n

\n# Train the XGBoost model

\n\nxgb.model <- xgb.train(params, dtrain, nround, watchlist)\n<\/p>\n\n\n\n

\n## [1] train-auc:0.700790
\n## [2] train-auc:0.720114
\n## [3] train-auc:0.735281
\n## [4] train-auc:0.741159
\n## [5] train-auc:0.748016
\n## [6] train-auc:0.752070
\n## [7] train-auc:0.754637
\n## [8] train-auc:0.759151
\n## [9] train-auc:0.762538
\n## [10] train-auc:0.769652
\n## [11] train-auc:0.776582
\n## [12] train-auc:0.780015
\n## [13] train-auc:0.782065
\n## [14] train-auc:0.782815
\n## [15] train-auc:0.788966
\n## [16] train-auc:0.791026
\n## [17] train-auc:0.793545
\n## [18] train-auc:0.797363
\n## [19] train-auc:0.799069
\n## [20] train-auc:0.802015
\n## [21] train-auc:0.802583
\n## [22] train-auc:0.806938
\n## [23] train-auc:0.808239
\n## [24] train-auc:0.811255
\n## [25] train-auc:0.813142
\n## [26] train-auc:0.816767
\n## [27] train-auc:0.817697
\n## [28] train-auc:0.820239
\n## [29] train-auc:0.821589
\n## [30] train-auc:0.823343
\n## [31] train-auc:0.823939
\n## [32] train-auc:0.825701
\n## [33] train-auc:0.827316
\n## [34] train-auc:0.829365
\n## [35] train-auc:0.832646
\n## [36] train-auc:0.833297
\n## [37] train-auc:0.837006
\n## [38] train-auc:0.838857
\n## [39] train-auc:0.839923
\n## [40] train-auc:0.842968
\n## [41] train-auc:0.844877
\n## [42] train-auc:0.845940
\n## [43] train-auc:0.846583
\n## [44] train-auc:0.847330
\n## [45] train-auc:0.848292
\n## [46] train-auc:0.850215
\n## [47] train-auc:0.851641
\n## [48] train-auc:0.852670
\n## [49] train-auc:0.854706
\n## [50] train-auc:0.855752
\n## [51] train-auc:0.856772
\n## [52] train-auc:0.857806
\n## [53] train-auc:0.860245
\n## [54] train-auc:0.861337
\n## [55] train-auc:0.864178
\n## [56] train-auc:0.865290
\n## [57] train-auc:0.865808
\n## [58] train-auc:0.866386
\n## [59] train-auc:0.867751
\n## [60] train-auc:0.870032
\n## [61] train-auc:0.870500
\n## [62] train-auc:0.872442
\n## [63] train-auc:0.873391
\n## [64] train-auc:0.875188
\n## [65] train-auc:0.877767
\n## [66] train-auc:0.879196
\n## [67] train-auc:0.880079
\n## [68] train-auc:0.879969
\n## [69] train-auc:0.880638
\n## [70] train-auc:0.881389
\n## [71] train-auc:0.882066
\n## [72] train-auc:0.882515
\n## [73] train-auc:0.883854
\n## [74] train-auc:0.884654
\n## [75] train-auc:0.885104
\n## [76] train-auc:0.885922
\n## [77] train-auc:0.887100
\n## [78] train-auc:0.888646
\n## [79] train-auc:0.889833
\n## [80] train-auc:0.890387
\n## [81] train-auc:0.891815
\n## [82] train-auc:0.892281
\n## [83] train-auc:0.894417
\n## [84] train-auc:0.895006
\n## [85] train-auc:0.897079
\n## [86] train-auc:0.899254
\n## [87] train-auc:0.901114
\n## [88] train-auc:0.902460
\n## [89] train-auc:0.902939
\n## [90] train-auc:0.903763
\n## [91] train-auc:0.903792
\n## [92] train-auc:0.904433
\n## [93] train-auc:0.904986
\n## [94] train-auc:0.907339
\n## [95] train-auc:0.907761\n<\/p>\n\n\n\n

\n# Note: Plot AUC on for the in-sample train \/ validation scores – this was a note for me at the time of writing this R file – I never did get around to plotting the AUC for the in-sample train \/ validation scores…\n<\/p>\n\n\n\n

What is nice about tree based models is that we can obtain importance<\/em> scores from the model and find which variables contributed most to the gain<\/em> in the model. The original paper explains more about the gain<\/em> in Algorithm 1<\/em> and Algorithm 3<\/em> here<\/a>.<\/p>\n\n\n\n

\n# We can obtain “feature” importance results from the model.
\nxgb.imp <- xgb.importance(model = xgb.model)
\nxgb.plot.importance(xgb.imp, top_n = 10)\n<\/p>\n\n\n\n

\"Time<\/figure>\n\n\n\n

That is, the XGBoost model found that the spike<\/code> was the most important variable. The spike<\/code> comes from the stl_features<\/code> function of the tsfeatures<\/code> package in R<\/code>. It computes various measures of trend and seasonality based on Seasonal and Trend Decomposition (STL) and measures the spikiness<\/code> of a time series based on the variance of the leave-one-out variances of component e_t<\/code>.<\/p>\n\n\n\n

The second variable is interesting also and comes from the compengine feature set<\/code> from the CompEngine<\/a> database. It groups variables as autocorrelation<\/em>, prediction<\/em>, stationarity<\/em>, distribution<\/em> and scaling<\/em>.<\/p>\n\n\n\n

The ARCH.LM<\/code> comes from the arch_stat<\/code> function of the tsfeatures<\/code> package and is based on the Lagrange Multiplier for Autoregressive Conditional Heteroscedasticity (ARCH) Engle 1982<\/a>.<\/p>\n\n\n\n

These are just a few of the variables the XGBoost model found to be the most important<\/em>. A full overview and more information of the variables used in the model can be found here<\/a>.<\/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><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Matthew Smith demonstrates the stl_features function of the tsfeatures package in R.<\/p>\n","protected":false},"author":372,"featured_media":44425,"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":[6989,806,6613,6988,6614,852,7811,6612,1045,5519,2536],"contributors-categories":[13694],"class_list":{"0":"post-95046","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-data-science","16":"tag-financial-data","17":"tag-financial-markets","18":"tag-jupyter-notebook","19":"tag-machine-learning","20":"tag-r-rstats","21":"tag-synthetic-time-series","22":"tag-tidyverse","23":"tag-time-series","24":"tag-visualization","25":"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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