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Time Series Classification Synthetic vs Real Financial Time Series

Distinguishing between real financial time series and synthetic time series using XGBoost


Excerpt

I was given a “Data Science” challenge as part of an interview in which I had to distinguish between real financial time series and synthetic time series. 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.


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 train and test data sets discussed later here where you should be able to run the final XGBoost 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 ggplot2. The time series features used here are taken from the following papers:

  • Large Scale Unusual Time Series Detection by R.Hyndman, E.Wang and N.Laptev
  • Visualising forecasting algorithm performance using time series instance spaces by Y.Kang, Rob.Hyndman and Kate Smith-Miles

You can check out my Jupyter Notebook version here.

I added a lot of notes to the code throughout the document which might be of additional interest.

Lets get started…

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 shhh command is useful for Jupyter Notebooks which outputs all the warning messages, adding shhh suppresses these warning messaged when loading in the packages. (In R markdown I can set warning = FALSE but there is no option on Notebooks. – that I know of – )

rm(list = ls())
options(scipen=999)
setwd('C:/Users/Matt/Desktop/Data Science Challenge')
shhh <- suppressPackageStartupMessages

shhh(library(dplyr))
library(readr)
library(TSrepr)
library(ggplot2)
library(data.table)
library(cluster)
library(clusterCrit)
library(fractalrock)
library(cowplot)
library(tidyr)
library(tidyquant)
library(lmtest)
library(aTSA)
library(tsoutliers)
library(tsfeatures)
library(xgboost)
library(caret)
library(purrr)

train_val <- read_csv("train.csv")
test <- read_csv("test.csv")

NOTE:

I have 2 data sets, the train_Val.csv for training and validation data set and the test.csv data set. I do not touch the test.csv data set until the very end in part 3. All the analysis and optimisation is performed only on the train_val.csv data set. The train_val.csv contains 12,000 observations and the test.csv contains 12,000 observations.

Part 1

The data was given to me in this format:

head(train_val[, 1:5], 1)
## # A tibble: 1 x 5
##   feature1 feature2 feature3 feature4 feature5
##      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
## 1  0.00629  0.00441  -0.0381   0.0253 -0.00658

The names of the columns are as follows:

colnames(train_val) %>%
  data.frame() %>%
  setNames(c("features")) %>%
  split(as.integer(gl(nrow(.), 20, nrow(.)))) %>%
  kable(caption = "Time series variables") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), font_size = 12)

The goal: Was to classify which financial time series were real vs which were synthetically created (by some algorithm I have no knowledge of how it generated the synthetic time series)

I re-arranged the data using the melt function in R, however I suggest anybody reading this to use the pivol_longer function from the tidyverse packages. The pivot_longer package was released a few weeks after writing the code for this problem.

Visit Matthew Smith R Blog to read the full article and download the R code:
https://lf0.com/post/synth-real-time-series/financial-time-series/

Disclosure: Interactive Brokers

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This material is from Matthew Smith and is being posted with permission from Matthew Smith. The views expressed in this material are solely those of the author and/or Matthew Smith and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

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