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Time Series Classification Synthetic vs Real Financial Time Series – Part IV

See Part IPart II and Part III in this article for instructions from Matthew Smith on which R packages and data sets you need.

I compute the 10 day rolling mean and standard deviation using the tq_mutate function from the tidyquant package. value corresponds to the returns of the financial time series and is plotted in blue with the 10 day rolling average and standard deviation plotted over the returns. (I use melt again here but look into the pivot_longer function for a more intuitive application)

# Rolling mean and standard deviations
# I only use a random sample of 1 of each class of the grouped observations to save on memory and to make the plot more readable.
# The rollowing window is 10 days
# I use the tq_mutate functionality from the “tidyquant” package to keep things in a “tidy” format as per the “tidyverse” ‘rules’.
# In the plot “value” is the returns, “mean_10” is the 10 day rolling mean and “sd_10” is the 10 day rolling standard deviation.

plot0 <- df %>%
filter(class == 0) %>%
as_tibble() %>%
group_by(row_id) %>%
nest() %>%
ungroup() %>%
sample_n(1) %>%
unnest() %>%
mutate(variable = as.Date(variable)) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = mean,
na.rm = TRUE,
col_rename = “mean_10”
) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = sd,
na.rm = TRUE,
col_rename = “sd_10”
) %>%
melt(measure.vars = 5:7) %>%
setNames(c(“row_id”, “class”, “data set”, “date”, “variable”, “value”)) %>%
ggplot(aes(x = date)) +
geom_line(aes(y = value, colour = variable)) +
ggtitle(“Synthetic Financial Time Series Rolling Mean and Standard Deviation”) +
theme_classic() +
scale_colour_manual(values = c(“#1f77b4”, “red”, “black”)) +
theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())

plot1 <- df %>%
filter(class == 1) %>%
as_tibble() %>%
group_by(row_id) %>%
nest() %>%
ungroup() %>%
sample_n(1) %>%
unnest() %>%
mutate(variable = as.Date(variable)) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = mean,
na.rm = TRUE,
col_rename = “mean_10”
) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = sd,
na.rm = TRUE,
col_rename = “sd_10”
) %>%
melt(measure.vars = 5:7) %>%
setNames(c(“row_id”, “class”, “data set”, “date”, “variable”, “value”)) %>%
ggplot(aes(x = date)) +
geom_line(aes(y = value, colour = variable)) +
ggtitle(“Real Financial Time Series Rolling Mean and Standard Deviation”) +
theme_classic() +
scale_colour_manual(values = c(“#1f77b4”, “red”, “black”)) +
theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())

plot_grid(plot0, plot1)

Visit Matthew Smith – R Blog to see the next step in his analysis.

Disclosure: Interactive Brokers

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