Exponential moving sum by group with time as the decay unit

Creates an exponential moving sum (EMS) UpdateByOp for each column in cols, using time as the decay unit.

Arguments

decay_time: ISO-8601-formatted duration string specifying the decay rate.
cols: String or list of strings denoting the column(s) to operate on. Can be renaming expressions, i.e. “new_col = col”. Default is to compute the exponential moving sum for all non-grouping columns.
operation_control: OperationControl that defines how special cases will behave. See ?op_control for more information.

Value

UpdateByOp to be used in a call to update_by().

Details

The formula used is $$a_i = e^{\frac{-dt_i}{\tau}}$$ $$\mathcal{S}_0 = x_0$$ $$\mathcal{S}_i = a_i*\mathcal{S}_{i-1} + x_i$$

Where:

$dt_i$ is the difference between time $t_i$ and $t_{i-1}$ in nanoseconds.
$\tau$ is decay_time in nanoseconds, an input parameter to the method.
$\mathcal{S}_i$ is the exponential moving sum of column $X$ at time step $i$.
$x_i$ is the current value.
$i$ denotes the time step, ranging from $i=1$ to $i = n-1$, where $n$ is the number of elements in $X$.

This function acts on aggregation groups specified with the by parameter of the update_by() caller function. The aggregation groups are defined by the unique combinations of values in the by columns. For example, if by = c("A", "B"), then the aggregation groups are defined by the unique combinations of values in the A and B columns.

This function, like other Deephaven uby functions, is a generator function. That is, its output is another function called an UpdateByOp intended to be used in a call to update_by(). This detail is typically hidden from the user. However, it is important to understand this detail for debugging purposes, as the output of a uby function can otherwise seem unexpected.

For more information, see the vignette on uby functions by running vignette("update_by").

Examples

if (FALSE) { # \dontrun{
library(rdeephaven)

# connecting to Deephaven server
client <- Client$new("localhost:10000", auth_type = "psk", auth_token = "my_secret_token")

# create data frame, push to server, retrieve TableHandle
df <- data.frame(
  timeCol = seq.POSIXt(as.POSIXct(Sys.Date()), as.POSIXct(Sys.Date() + 0.01), by = "1 sec")[1:500],
  boolCol = sample(c(TRUE, FALSE), 500, TRUE),
  col1 = sample(10000, size = 500, replace = TRUE),
  col2 = sample(10000, size = 500, replace = TRUE),
  col3 = 1:500
)
th <- client$import_table(df)

# compute 10-second exponential moving sum of col1 and col2
th1 <- th$
  update_by(uby_ems_time(ts_col = "timeCol", decay_time = "PT10s", cols = c("col1Ems = col1", "col2Ems = col2")))

# compute 5-second exponential moving sum of col1 and col2, grouped by boolCol
th2 <- th$
  update_by(uby_ems_time(ts_col = "timeCol", decay_time = "PT5s", cols = c("col1Ems = col1", "col2Ems = col2")), by = "boolCol")

# compute 20-second exponential moving sum of col1 and col2, grouped by boolCol and parity of col3
th3 <- th$
  update("col3Parity = col3 % 2")$
  update_by(uby_ems_time(ts_col = "timeCol", decay_time = "PT20s", cols = c("col1Ems = col1", "col2Ems = col2")), by = c("boolCol", "col3Parity"))

client$close()
} # }