it should still fix some issue It works for me. We don't need summary nor by.column (there is only one column anyways). The resulting range is c(90, 90) to 3 decimals. library(xts)
f < function (x) {
res < lm(x ~ time(x))
atan(coef(res)[[2]]) * 180 / pi
}
r < rollapplyr(pk, 14, f)
round(range(r, na.rm = TRUE), 3)
## [1] 90 90
r < rollapplyr(k * 14 * pk / max(pk), 14, f)
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Rolling window from timeseries
Date : March 29 2020, 07:55 AM
will help you I am wondering if there is a simple and neat way to create a rolling window representation from a timeseries data, using Pandas (etc)? , How about: pd.DataFrame({i: x.shift(i) for i in range(5)}).dropna()

Time series with scala and spark. Rolling window
Tag : scala , By : Thomas Plunkett
Date : March 29 2020, 07:55 AM
help you fix your problem This is a perfect application for windowfunctions. By using rangeBetween you can set your sliding window to 20s. Note that in the example below no partitioning is specified (no partitionBy). Without a partitioning, this code will not scale: import ss.implicits._
val df = Seq(
(225, 1.5),
(245, 0.5),
(300, 2.4),
(319, 1.2),
(320, 4.6)
).toDF("seconds", "value")
val window = Window.orderBy($"seconds").rangeBetween(20L, 0L) // add partitioning here
df
.withColumn("num_row_in_window", sum(lit(1)).over(window))
.withColumn("sum_values_in_window", sum($"value").over(window))
.show()
+++++
secondsvaluenum_row_in_windowsum_values_in_window
+++++
 225 1.5 1 1.5
 245 0.5 2 2.0
 300 2.4 1 2.4
 319 1.2 2 3.6
 320 4.6 3 8.2
+++++

Rolling window over irregular time series
Date : March 29 2020, 07:55 AM

optimized rolling functions on irregular time series with timebased window
Date : March 29 2020, 07:55 AM

R: Compute a rolling sum on irregular time series grouped by id variables with timebased window
Date : March 29 2020, 07:55 AM

