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/